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apache / spark / master / . / python / pyspark / sql / functions / builtin.py
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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
A collections of builtin functions
"""
import inspect
import decimal
import sys
import functools
import warnings
from typing import (
Any,
cast,
Callable,
Mapping,
Sequence,
Iterable,
overload,
Optional,
Tuple,
Type,
TYPE_CHECKING,
Union,
ValuesView,
)
from pyspark.errors import PySparkTypeError, PySparkValueError
from pyspark.errors.utils import _with_origin
from pyspark.sql.column import Column
from pyspark.sql.types import (
ArrayType,
ByteType,
DataType,
StringType,
StructType,
NumericType,
_from_numpy_type,
)
# Keep UserDefinedFunction import for backwards compatible import; moved in SPARK-22409
from pyspark.sql.udf import UserDefinedFunction, _create_py_udf # noqa: F401
from pyspark.sql.udtf import AnalyzeArgument, AnalyzeResult # noqa: F401
from pyspark.sql.udtf import OrderingColumn, PartitioningColumn, SelectedColumn # noqa: F401
from pyspark.sql.udtf import SkipRestOfInputTableException # noqa: F401
from pyspark.sql.udtf import UserDefinedTableFunction, _create_py_udtf, _create_pyarrow_udtf
# Keep pandas_udf and PandasUDFType import for backwards compatible import; moved in SPARK-28264
from pyspark.sql.pandas.functions import ( # noqa: F401
arrow_udf, # noqa: F401
pandas_udf, # noqa: F401
ArrowUDFType, # noqa: F401
PandasUDFType, # noqa: F401
) # noqa: F401
from pyspark.sql.utils import (
to_str as _to_str,
try_remote_functions as _try_remote_functions,
get_active_spark_context as _get_active_spark_context,
enum_to_value as _enum_to_value,
)
if TYPE_CHECKING:
from pyspark import SparkContext
from pyspark.sql.dataframe import DataFrame
from pyspark.sql._typing import (
ColumnOrName,
DataTypeOrString,
UserDefinedFunctionLike,
)
# Note to developers: all of PySpark functions here take string as column names whenever possible.
# Namely, if columns are referred as arguments, they can always be both Column or string,
# even though there might be few exceptions for legacy or inevitable reasons.
# If you are fixing other language APIs together, also please note that Scala side is not the case
# since it requires making every single overridden definition.
def _get_jvm_function(name: str, sc: "SparkContext") -> Callable:
"""
Retrieves JVM function identified by name from
Java gateway associated with sc.
"""
assert sc._jvm is not None
return getattr(getattr(sc._jvm, "org.apache.spark.sql.functions"), name)
def _invoke_function(name: str, *args: Any) -> Column:
"""
Invokes JVM function identified by name with args
and wraps the result with :class:`~pyspark.sql.Column`.
"""
from pyspark import SparkContext
assert SparkContext._active_spark_context is not None
jf = _get_jvm_function(name, SparkContext._active_spark_context)
return Column(jf(*args))
def _invoke_function_over_columns(name: str, *cols: "ColumnOrName") -> Column:
"""
Invokes n-ary JVM function identified by name
and wraps the result with :class:`~pyspark.sql.Column`.
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(name, *(_to_java_column(col) for col in cols))
def _invoke_function_over_seq_of_columns(name: str, cols: "Iterable[ColumnOrName]") -> Column:
"""
Invokes unary JVM function identified by name with
and wraps the result with :class:`~pyspark.sql.Column`.
"""
from pyspark.sql.classic.column import _to_java_column, _to_seq
sc = _get_active_spark_context()
return _invoke_function(name, _to_seq(sc, cols, _to_java_column))
def _invoke_binary_math_function(name: str, col1: Any, col2: Any) -> Column:
"""
Invokes binary JVM math function identified by name
and wraps the result with :class:`~pyspark.sql.Column`.
"""
from pyspark.sql.classic.column import _to_java_column, _create_column_from_literal
# For legacy reasons, the arguments here can be implicitly converted into column
cols = [
_to_java_column(c) if isinstance(c, (str, Column)) else _create_column_from_literal(c)
for c in (col1, col2)
]
return _invoke_function(name, *cols)
def _options_to_str(options: Optional[Mapping[str, Any]] = None) -> Mapping[str, Optional[str]]:
if options:
return {key: _to_str(value) for (key, value) in options.items()}
return {}
@_try_remote_functions
def lit(col: Any) -> Column:
"""
Creates a :class:`~pyspark.sql.Column` of literal value.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column`, str, int, float, bool or list, NumPy literals or ndarray.
the value to make it as a PySpark literal. If a column is passed,
it returns the column as is.
.. versionchanged:: 3.4.0
Since 3.4.0, it supports the list type.
Returns
-------
:class:`~pyspark.sql.Column`
the literal instance.
Examples
--------
Example 1: Creating a literal column with an integer value.
>>> import pyspark.sql.functions as sf
>>> df = spark.range(1)
>>> df.select(sf.lit(5).alias('height'), df.id).show()
+------+---+
|height| id|
+------+---+
| 5| 0|
+------+---+
Example 2: Creating a literal column from a list.
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.lit([1, 2, 3])).show()
+--------------+
|array(1, 2, 3)|
+--------------+
| [1, 2, 3]|
+--------------+
Example 3: Creating a literal column from a string.
>>> import pyspark.sql.functions as sf
>>> df = spark.range(1)
>>> df.select(sf.lit("PySpark").alias('framework'), df.id).show()
+---------+---+
|framework| id|
+---------+---+
| PySpark| 0|
+---------+---+
Example 4: Creating a literal column from a boolean value.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(True, "Yes"), (False, "No")], ["flag", "response"])
>>> df.select(sf.lit(False).alias('is_approved'), df.response).show()
+-----------+--------+
|is_approved|response|
+-----------+--------+
| false| Yes|
| false| No|
+-----------+--------+
Example 5: Creating literal columns from Numpy scalar.
>>> from pyspark.sql import functions as sf
>>> import numpy as np # doctest: +SKIP
>>> spark.range(1).select(
... sf.lit(np.bool_(True)),
... sf.lit(np.int64(123)),
... sf.lit(np.float64(0.456)),
... sf.lit(np.str_("xyz"))
... ).show() # doctest: +SKIP
+----+---+-----+---+
|true|123|0.456|xyz|
+----+---+-----+---+
|true|123|0.456|xyz|
+----+---+-----+---+
Example 6: Creating literal columns from Numpy ndarray.
>>> from pyspark.sql import functions as sf
>>> import numpy as np # doctest: +SKIP
>>> spark.range(1).select(
... sf.lit(np.array([True, False], np.bool_)),
... sf.lit(np.array([], np.int8)),
... sf.lit(np.array([1.5, 0.1], np.float64)),
... sf.lit(np.array(["a", "b", "c"], np.str_)),
... ).show() # doctest: +SKIP
+------------------+-------+-----------------+--------------------+
|ARRAY(true, false)|ARRAY()|ARRAY(1.5D, 0.1D)|ARRAY('a', 'b', 'c')|
+------------------+-------+-----------------+--------------------+
| [true, false]| []| [1.5, 0.1]| [a, b, c]|
+------------------+-------+-----------------+--------------------+
"""
from pyspark.testing.utils import have_numpy
if isinstance(col, Column):
return col
elif isinstance(col, list):
if any(isinstance(c, Column) for c in col):
raise PySparkValueError(
errorClass="COLUMN_IN_LIST", messageParameters={"func_name": "lit"}
)
return array(*[lit(item) for item in col])
elif have_numpy:
import numpy as np
if isinstance(col, np.generic):
dt = _from_numpy_type(col.dtype)
if dt is None:
raise PySparkTypeError(
errorClass="UNSUPPORTED_NUMPY_ARRAY_SCALAR",
messageParameters={"dtype": col.dtype.name},
)
if isinstance(dt, NumericType):
# NumpyScalarConverter for Py4J converts numeric scalar to Python scalar.
# E.g. numpy.int64(1) is converted to int(1).
# So, we need to cast it back to the original type.
return _invoke_function("lit", col).astype(dt).alias(str(col))
else:
return _invoke_function("lit", col)
elif isinstance(col, np.ndarray) and col.ndim == 1:
dt = _from_numpy_type(col.dtype)
if dt is None:
raise PySparkTypeError(
errorClass="UNSUPPORTED_NUMPY_ARRAY_SCALAR",
messageParameters={"dtype": col.dtype.name},
)
if isinstance(dt, ByteType):
# NumpyArrayConverter for Py4J converts Array[Byte] to Array[Short].
# Cast it back to ByteType.
return _invoke_function("lit", col).cast(ArrayType(dt))
else:
return _invoke_function("lit", col)
return _invoke_function("lit", _enum_to_value(col))
@_try_remote_functions
@_with_origin
def col(col: str) -> Column:
"""
Returns a :class:`~pyspark.sql.Column` based on the given column name.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : column name
the name for the column
Returns
-------
:class:`~pyspark.sql.Column`
the corresponding column instance.
Examples
--------
>>> col('x')
Column<'x'>
>>> column('x')
Column<'x'>
"""
return _invoke_function("col", col)
column = col
@_try_remote_functions
def asc(col: "ColumnOrName") -> Column:
"""
Returns a sort expression for the target column in ascending order.
This function is used in `sort` and `orderBy` functions.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Target column to sort by in the ascending order.
Returns
-------
:class:`~pyspark.sql.Column`
The column specifying the sort order.
See Also
--------
:meth:`pyspark.sql.functions.asc_nulls_first`
:meth:`pyspark.sql.functions.asc_nulls_last`
Examples
--------
Example 1: Sort DataFrame by 'id' column in ascending order.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id', 'value'])
>>> df.sort(sf.asc("id")).show()
+---+-----+
| id|value|
+---+-----+
| 2| C|
| 3| A|
| 4| B|
+---+-----+
Example 2: Use `asc` in `orderBy` function to sort the DataFrame.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id', 'value'])
>>> df.orderBy(sf.asc("value")).show()
+---+-----+
| id|value|
+---+-----+
| 3| A|
| 4| B|
| 2| C|
+---+-----+
Example 3: Combine `asc` with `desc` to sort by multiple columns.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(2, 'A', 4), (1, 'B', 3), (3, 'A', 2)],
... ['id', 'group', 'value'])
>>> df.sort(sf.asc("group"), sf.desc("value")).show()
+---+-----+-----+
| id|group|value|
+---+-----+-----+
| 2| A| 4|
| 3| A| 2|
| 1| B| 3|
+---+-----+-----+
Example 4: Implement `asc` from column expression.
>>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id', 'value'])
>>> df.sort(df.id.asc()).show()
+---+-----+
| id|value|
+---+-----+
| 2| C|
| 3| A|
| 4| B|
+---+-----+
"""
return col.asc() if isinstance(col, Column) else _invoke_function("asc", col)
@_try_remote_functions
def desc(col: "ColumnOrName") -> Column:
"""
Returns a sort expression for the target column in descending order.
This function is used in `sort` and `orderBy` functions.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Target column to sort by in the descending order.
Returns
-------
:class:`~pyspark.sql.Column`
The column specifying the sort order.
See Also
--------
:meth:`pyspark.sql.functions.desc_nulls_first`
:meth:`pyspark.sql.functions.desc_nulls_last`
Examples
--------
Example 1: Sort DataFrame by 'id' column in descending order.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id', 'value'])
>>> df.sort(sf.desc("id")).show()
+---+-----+
| id|value|
+---+-----+
| 4| B|
| 3| A|
| 2| C|
+---+-----+
Example 2: Use `desc` in `orderBy` function to sort the DataFrame.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id', 'value'])
>>> df.orderBy(sf.desc("value")).show()
+---+-----+
| id|value|
+---+-----+
| 2| C|
| 4| B|
| 3| A|
+---+-----+
Example 3: Combine `asc` with `desc` to sort by multiple columns.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(2, 'A', 4), (1, 'B', 3), (3, 'A', 2)],
... ['id', 'group', 'value'])
>>> df.sort(sf.desc("group"), sf.asc("value")).show()
+---+-----+-----+
| id|group|value|
+---+-----+-----+
| 1| B| 3|
| 3| A| 2|
| 2| A| 4|
+---+-----+-----+
Example 4: Implement `desc` from column expression.
>>> df = spark.createDataFrame([(4, 'B'), (3, 'A'), (2, 'C')], ['id', 'value'])
>>> df.sort(df.id.desc()).show()
+---+-----+
| id|value|
+---+-----+
| 4| B|
| 3| A|
| 2| C|
+---+-----+
"""
return col.desc() if isinstance(col, Column) else _invoke_function("desc", col)
@_try_remote_functions
def sqrt(col: "ColumnOrName") -> Column:
"""
Computes the square root of the specified float value.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
column for computed results.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (-1), (0), (1), (4), (NULL) AS TAB(value)"
... ).select("*", sf.sqrt("value")).show()
+-----+-----------+
|value|SQRT(value)|
+-----+-----------+
| -1| NaN|
| 0| 0.0|
| 1| 1.0|
| 4| 2.0|
| NULL| NULL|
+-----+-----------+
"""
return _invoke_function_over_columns("sqrt", col)
@_try_remote_functions
def try_add(left: "ColumnOrName", right: "ColumnOrName") -> Column:
"""
Returns the sum of `left`and `right` and the result is null on overflow.
The acceptable input types are the same with the `+` operator.
.. versionadded:: 3.5.0
Parameters
----------
left : :class:`~pyspark.sql.Column` or column name
right : :class:`~pyspark.sql.Column` or column name
Examples
--------
Example 1: Integer plus Integer.
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [(1982, 15), (1990, 2)], ["birth", "age"]
... ).select("*", sf.try_add("birth", "age")).show()
+-----+---+-------------------+
|birth|age|try_add(birth, age)|
+-----+---+-------------------+
| 1982| 15| 1997|
| 1990| 2| 1992|
+-----+---+-------------------+
Example 2: Date plus Integer.
>>> import pyspark.sql.functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (DATE('2015-09-30')) AS TAB(date)"
... ).select("*", sf.try_add("date", sf.lit(1))).show()
+----------+----------------+
| date|try_add(date, 1)|
+----------+----------------+
|2015-09-30| 2015-10-01|
+----------+----------------+
Example 3: Date plus Interval.
>>> import pyspark.sql.functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (DATE('2015-09-30'), INTERVAL 1 YEAR) AS TAB(date, itvl)"
... ).select("*", sf.try_add("date", "itvl")).show()
+----------+-----------------+-------------------+
| date| itvl|try_add(date, itvl)|
+----------+-----------------+-------------------+
|2015-09-30|INTERVAL '1' YEAR| 2016-09-30|
+----------+-----------------+-------------------+
Example 4: Interval plus Interval.
>>> import pyspark.sql.functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (INTERVAL 1 YEAR, INTERVAL 2 YEAR) AS TAB(itvl1, itvl2)"
... ).select("*", sf.try_add("itvl1", "itvl2")).show()
+-----------------+-----------------+---------------------+
| itvl1| itvl2|try_add(itvl1, itvl2)|
+-----------------+-----------------+---------------------+
|INTERVAL '1' YEAR|INTERVAL '2' YEAR| INTERVAL '3' YEAR|
+-----------------+-----------------+---------------------+
Example 5: Overflow results in NULL when ANSI mode is on
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
... spark.range(1).select(sf.try_add(sf.lit(sys.maxsize), sf.lit(sys.maxsize))).show()
... finally:
... spark.conf.set("spark.sql.ansi.enabled", origin)
+-------------------------------------------------+
|try_add(9223372036854775807, 9223372036854775807)|
+-------------------------------------------------+
| NULL|
+-------------------------------------------------+
"""
return _invoke_function_over_columns("try_add", left, right)
@_try_remote_functions
def try_avg(col: "ColumnOrName") -> Column:
"""
Returns the mean calculated from values of a group and the result is null on overflow.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Examples
--------
Example 1: Calculating the average age
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1982, 15), (1990, 2)], ["birth", "age"])
>>> df.select(sf.try_avg("age")).show()
+------------+
|try_avg(age)|
+------------+
| 8.5|
+------------+
Example 2: Calculating the average age with None
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1982, None), (1990, 2), (2000, 4)], ["birth", "age"])
>>> df.select(sf.try_avg("age")).show()
+------------+
|try_avg(age)|
+------------+
| 3.0|
+------------+
Example 3: Overflow results in NULL when ANSI mode is on
>>> from decimal import Decimal
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
... df = spark.createDataFrame(
... [(Decimal("1" * 38),), (Decimal(0),)], "number DECIMAL(38, 0)")
... df.select(sf.try_avg(df.number)).show()
... finally:
... spark.conf.set("spark.sql.ansi.enabled", origin)
+---------------+
|try_avg(number)|
+---------------+
| NULL|
+---------------+
"""
return _invoke_function_over_columns("try_avg", col)
@_try_remote_functions
def try_divide(left: "ColumnOrName", right: "ColumnOrName") -> Column:
"""
Returns `dividend`/`divisor`. It always performs floating point division. Its result is
always null if `divisor` is 0.
.. versionadded:: 3.5.0
Parameters
----------
left : :class:`~pyspark.sql.Column` or column name
dividend
right : :class:`~pyspark.sql.Column` or column name
divisor
Examples
--------
Example 1: Integer divided by Integer.
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [(6000, 15), (1990, 2), (1234, 0)], ["a", "b"]
... ).select("*", sf.try_divide("a", "b")).show()
+----+---+----------------+
| a| b|try_divide(a, b)|
+----+---+----------------+
|6000| 15| 400.0|
|1990| 2| 995.0|
|1234| 0| NULL|
+----+---+----------------+
Example 2: Interval divided by Integer.
>>> import pyspark.sql.functions as sf
>>> df = spark.range(4).select(sf.make_interval(sf.lit(1)).alias("itvl"), "id")
>>> df.select("*", sf.try_divide("itvl", "id")).show()
+-------+---+--------------------+
| itvl| id|try_divide(itvl, id)|
+-------+---+--------------------+
|1 years| 0| NULL|
|1 years| 1| 1 years|
|1 years| 2| 6 months|
|1 years| 3| 4 months|
+-------+---+--------------------+
Example 3: Exception during division, resulting in NULL when ANSI mode is on
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
... spark.range(1).select(sf.try_divide("id", sf.lit(0))).show()
... finally:
... spark.conf.set("spark.sql.ansi.enabled", origin)
+-----------------+
|try_divide(id, 0)|
+-----------------+
| NULL|
+-----------------+
"""
return _invoke_function_over_columns("try_divide", left, right)
@_try_remote_functions
def try_mod(left: "ColumnOrName", right: "ColumnOrName") -> Column:
"""
Returns the remainder after `dividend`/`divisor`. Its result is
always null if `divisor` is 0.
.. versionadded:: 4.0.0
Parameters
----------
left : :class:`~pyspark.sql.Column` or column name
dividend
right : :class:`~pyspark.sql.Column` or column name
divisor
Examples
--------
Example 1: Integer divided by Integer.
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [(6000, 15), (3, 2), (1234, 0)], ["a", "b"]
... ).select("*", sf.try_mod("a", "b")).show()
+----+---+-------------+
| a| b|try_mod(a, b)|
+----+---+-------------+
|6000| 15| 0|
| 3| 2| 1|
|1234| 0| NULL|
+----+---+-------------+
Example 2: Exception during division, resulting in NULL when ANSI mode is on
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
... spark.range(1).select(sf.try_mod("id", sf.lit(0))).show()
... finally:
... spark.conf.set("spark.sql.ansi.enabled", origin)
+--------------+
|try_mod(id, 0)|
+--------------+
| NULL|
+--------------+
"""
return _invoke_function_over_columns("try_mod", left, right)
@_try_remote_functions
def try_multiply(left: "ColumnOrName", right: "ColumnOrName") -> Column:
"""
Returns `left`*`right` and the result is null on overflow. The acceptable input types are the
same with the `*` operator.
.. versionadded:: 3.5.0
Parameters
----------
left : :class:`~pyspark.sql.Column` or column name
multiplicand
right : :class:`~pyspark.sql.Column` or column name
multiplier
Examples
--------
Example 1: Integer multiplied by Integer.
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [(6000, 15), (1990, 2)], ["a", "b"]
... ).select("*", sf.try_multiply("a", "b")).show()
+----+---+------------------+
| a| b|try_multiply(a, b)|
+----+---+------------------+
|6000| 15| 90000|
|1990| 2| 3980|
+----+---+------------------+
Example 2: Interval multiplied by Integer.
>>> import pyspark.sql.functions as sf
>>> df = spark.range(6).select(sf.make_interval(sf.col("id"), sf.lit(3)).alias("itvl"), "id")
>>> df.select("*", sf.try_multiply("itvl", "id")).show()
+----------------+---+----------------------+
| itvl| id|try_multiply(itvl, id)|
+----------------+---+----------------------+
| 3 months| 0| 0 seconds|
|1 years 3 months| 1| 1 years 3 months|
|2 years 3 months| 2| 4 years 6 months|
|3 years 3 months| 3| 9 years 9 months|
|4 years 3 months| 4| 17 years|
|5 years 3 months| 5| 26 years 3 months|
+----------------+---+----------------------+
Example 3: Overflow results in NULL when ANSI mode is on
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
... spark.range(1).select(sf.try_multiply(sf.lit(sys.maxsize), sf.lit(sys.maxsize))).show()
... finally:
... spark.conf.set("spark.sql.ansi.enabled", origin)
+------------------------------------------------------+
|try_multiply(9223372036854775807, 9223372036854775807)|
+------------------------------------------------------+
| NULL|
+------------------------------------------------------+
"""
return _invoke_function_over_columns("try_multiply", left, right)
@_try_remote_functions
def try_subtract(left: "ColumnOrName", right: "ColumnOrName") -> Column:
"""
Returns `left`-`right` and the result is null on overflow. The acceptable input types are the
same with the `-` operator.
.. versionadded:: 3.5.0
Parameters
----------
left : :class:`~pyspark.sql.Column` or column name
right : :class:`~pyspark.sql.Column` or column name
Examples
--------
Example 1: Integer minus Integer.
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [(1982, 15), (1990, 2)], ["birth", "age"]
... ).select("*", sf.try_subtract("birth", "age")).show()
+-----+---+------------------------+
|birth|age|try_subtract(birth, age)|
+-----+---+------------------------+
| 1982| 15| 1967|
| 1990| 2| 1988|
+-----+---+------------------------+
Example 2: Date minus Integer.
>>> import pyspark.sql.functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (DATE('2015-10-01')) AS TAB(date)"
... ).select("*", sf.try_subtract("date", sf.lit(1))).show()
+----------+---------------------+
| date|try_subtract(date, 1)|
+----------+---------------------+
|2015-10-01| 2015-09-30|
+----------+---------------------+
Example 3: Date minus Interval.
>>> import pyspark.sql.functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (DATE('2015-09-30'), INTERVAL 1 YEAR) AS TAB(date, itvl)"
... ).select("*", sf.try_subtract("date", "itvl")).show()
+----------+-----------------+------------------------+
| date| itvl|try_subtract(date, itvl)|
+----------+-----------------+------------------------+
|2015-09-30|INTERVAL '1' YEAR| 2014-09-30|
+----------+-----------------+------------------------+
Example 4: Interval minus Interval.
>>> import pyspark.sql.functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (INTERVAL 1 YEAR, INTERVAL 2 YEAR) AS TAB(itvl1, itvl2)"
... ).select("*", sf.try_subtract("itvl1", "itvl2")).show()
+-----------------+-----------------+--------------------------+
| itvl1| itvl2|try_subtract(itvl1, itvl2)|
+-----------------+-----------------+--------------------------+
|INTERVAL '1' YEAR|INTERVAL '2' YEAR| INTERVAL '-1' YEAR|
+-----------------+-----------------+--------------------------+
Example 5: Overflow results in NULL when ANSI mode is on
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
... spark.range(1).select(sf.try_subtract(sf.lit(-sys.maxsize), sf.lit(sys.maxsize))).show()
... finally:
... spark.conf.set("spark.sql.ansi.enabled", origin)
+-------------------------------------------------------+
|try_subtract(-9223372036854775807, 9223372036854775807)|
+-------------------------------------------------------+
| NULL|
+-------------------------------------------------------+
"""
return _invoke_function_over_columns("try_subtract", left, right)
@_try_remote_functions
def try_sum(col: "ColumnOrName") -> Column:
"""
Returns the sum calculated from values of a group and the result is null on overflow.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Examples
--------
Example 1: Calculating the sum of values in a column
>>> from pyspark.sql import functions as sf
>>> spark.range(10).select(sf.try_sum("id")).show()
+-----------+
|try_sum(id)|
+-----------+
| 45|
+-----------+
Example 2: Using a plus expression together to calculate the sum
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, 2), (3, 4)], ["A", "B"])
>>> df.select(sf.try_sum(sf.col("A") + sf.col("B"))).show()
+----------------+
|try_sum((A + B))|
+----------------+
| 10|
+----------------+
Example 3: Calculating the summation of ages with None
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1982, None), (1990, 2), (2000, 4)], ["birth", "age"])
>>> df.select(sf.try_sum("age")).show()
+------------+
|try_sum(age)|
+------------+
| 6|
+------------+
Example 4: Overflow results in NULL when ANSI mode is on
>>> from decimal import Decimal
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
... df = spark.createDataFrame([(Decimal("1" * 38),)] * 10, "number DECIMAL(38, 0)")
... df.select(sf.try_sum(df.number)).show()
... finally:
... spark.conf.set("spark.sql.ansi.enabled", origin)
+---------------+
|try_sum(number)|
+---------------+
| NULL|
+---------------+
"""
return _invoke_function_over_columns("try_sum", col)
@_try_remote_functions
def abs(col: "ColumnOrName") -> Column:
"""
Mathematical Function: Computes the absolute value of the given column or expression.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column or expression to compute the absolute value on.
Returns
-------
:class:`~pyspark.sql.Column`
A new column object representing the absolute value of the input.
Examples
--------
Example 1: Compute the absolute value of a long column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-1,), (-2,), (-3,), (None,)], ["value"])
>>> df.select("*", sf.abs(df.value)).show()
+-----+----------+
|value|abs(value)|
+-----+----------+
| -1| 1|
| -2| 2|
| -3| 3|
| NULL| NULL|
+-----+----------+
Example 2: Compute the absolute value of a double column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-1.5,), (-2.5,), (None,), (float("nan"),)], ["value"])
>>> df.select("*", sf.abs(df.value)).show()
+-----+----------+
|value|abs(value)|
+-----+----------+
| -1.5| 1.5|
| -2.5| 2.5|
| NULL| NULL|
| NaN| NaN|
+-----+----------+
Example 3: Compute the absolute value of an expression
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, 1), (2, -2), (3, 3)], ["id", "value"])
>>> df.select("*", sf.abs(df.id - df.value)).show()
+---+-----+-----------------+
| id|value|abs((id - value))|
+---+-----+-----------------+
| 1| 1| 0|
| 2| -2| 4|
| 3| 3| 0|
+---+-----+-----------------+
"""
return _invoke_function_over_columns("abs", col)
@_try_remote_functions
def mode(col: "ColumnOrName", deterministic: bool = False) -> Column:
"""
Returns the most frequent value in a group.
.. versionadded:: 3.4.0
.. versionchanged:: 4.0.0
Supports deterministic argument.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
deterministic : bool, optional
if there are multiple equally-frequent results then return the lowest (defaults to false).
Returns
-------
:class:`~pyspark.sql.Column`
the most frequent value in a group.
Notes
-----
Supports Spark Connect.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... ("Java", 2012, 20000), ("dotNET", 2012, 5000),
... ("Java", 2012, 20000), ("dotNET", 2012, 5000),
... ("dotNET", 2013, 48000), ("Java", 2013, 30000)],
... schema=("course", "year", "earnings"))
>>> df.groupby("course").agg(sf.mode("year")).sort("course").show()
+------+----------+
|course|mode(year)|
+------+----------+
| Java| 2012|
|dotNET| 2012|
+------+----------+
When multiple values have the same greatest frequency then either any of values is returned if
deterministic is false or is not defined, or the lowest value is returned if deterministic is
true.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-10,), (0,), (10,)], ["col"])
>>> df.select(sf.mode("col", False)).show() # doctest: +SKIP
+---------+
|mode(col)|
+---------+
| 0|
+---------+
>>> df.select(sf.mode("col", True)).show()
+---------------------------------------+
|mode() WITHIN GROUP (ORDER BY col DESC)|
+---------------------------------------+
| -10|
+---------------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("mode", _to_java_column(col), _enum_to_value(deterministic))
@_try_remote_functions
def max(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the maximum value of the expression in a group.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column on which the maximum value is computed.
Returns
-------
:class:`~pyspark.sql.Column`
A column that contains the maximum value computed.
See Also
--------
:meth:`pyspark.sql.functions.min`
:meth:`pyspark.sql.functions.avg`
:meth:`pyspark.sql.functions.sum`
Notes
-----
- Null values are ignored during the computation.
- NaN values are larger than any other numeric value.
Examples
--------
Example 1: Compute the maximum value of a numeric column
>>> import pyspark.sql.functions as sf
>>> df = spark.range(10)
>>> df.select(sf.max(df.id)).show()
+-------+
|max(id)|
+-------+
| 9|
+-------+
Example 2: Compute the maximum value of a string column
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("A",), ("B",), ("C",)], ["value"])
>>> df.select(sf.max(df.value)).show()
+----------+
|max(value)|
+----------+
| C|
+----------+
Example 3: Compute the maximum value of a column in a grouped DataFrame
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("A", 1), ("A", 2), ("B", 3), ("B", 4)], ["key", "value"])
>>> df.groupBy("key").agg(sf.max(df.value)).show()
+---+----------+
|key|max(value)|
+---+----------+
| A| 2|
| B| 4|
+---+----------+
Example 4: Compute the maximum value of multiple columns in a grouped DataFrame
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame(
... [("A", 1, 2), ("A", 2, 3), ("B", 3, 4), ("B", 4, 5)], ["key", "value1", "value2"])
>>> df.groupBy("key").agg(sf.max("value1"), sf.max("value2")).show()
+---+-----------+-----------+
|key|max(value1)|max(value2)|
+---+-----------+-----------+
| A| 2| 3|
| B| 4| 5|
+---+-----------+-----------+
Example 5: Compute the maximum value of a column with null values
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1,), (2,), (None,)], ["value"])
>>> df.select(sf.max(df.value)).show()
+----------+
|max(value)|
+----------+
| 2|
+----------+
Example 6: Compute the maximum value of a column with "NaN" values
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1.1,), (float("nan"),), (3.3,)], ["value"])
>>> df.select(sf.max(df.value)).show()
+----------+
|max(value)|
+----------+
| NaN|
+----------+
"""
return _invoke_function_over_columns("max", col)
@_try_remote_functions
def min(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the minimum value of the expression in a group.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column on which the minimum value is computed.
Returns
-------
:class:`~pyspark.sql.Column`
A column that contains the minimum value computed.
See Also
--------
:meth:`pyspark.sql.functions.max`
:meth:`pyspark.sql.functions.avg`
:meth:`pyspark.sql.functions.sum`
Examples
--------
Example 1: Compute the minimum value of a numeric column
>>> import pyspark.sql.functions as sf
>>> df = spark.range(10)
>>> df.select(sf.min(df.id)).show()
+-------+
|min(id)|
+-------+
| 0|
+-------+
Example 2: Compute the minimum value of a string column
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("Alice",), ("Bob",), ("Charlie",)], ["name"])
>>> df.select(sf.min("name")).show()
+---------+
|min(name)|
+---------+
| Alice|
+---------+
Example 3: Compute the minimum value of a column with null values
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1,), (None,), (3,)], ["value"])
>>> df.select(sf.min("value")).show()
+----------+
|min(value)|
+----------+
| 1|
+----------+
Example 4: Compute the minimum value of a column in a grouped DataFrame
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("Alice", 1), ("Alice", 2), ("Bob", 3)], ["name", "value"])
>>> df.groupBy("name").agg(sf.min("value")).show()
+-----+----------+
| name|min(value)|
+-----+----------+
|Alice| 1|
| Bob| 3|
+-----+----------+
Example 5: Compute the minimum value of a column in a DataFrame with multiple columns
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame(
... [("Alice", 1, 100), ("Bob", 2, 200), ("Charlie", 3, 300)],
... ["name", "value1", "value2"])
>>> df.select(sf.min("value1"), sf.min("value2")).show()
+-----------+-----------+
|min(value1)|min(value2)|
+-----------+-----------+
| 1| 100|
+-----------+-----------+
"""
return _invoke_function_over_columns("min", col)
@_try_remote_functions
def max_by(col: "ColumnOrName", ord: "ColumnOrName") -> Column:
"""
Returns the value from the `col` parameter that is associated with the maximum value
from the `ord` parameter. This function is often used to find the `col` parameter value
corresponding to the maximum `ord` parameter value within each group when used with groupBy().
.. versionadded:: 3.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The function is non-deterministic so the output order can be different for those
associated the same values of `col`.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The column representing the values to be returned. This could be the column instance
or the column name as string.
ord : :class:`~pyspark.sql.Column` or column name
The column that needs to be maximized. This could be the column instance
or the column name as string.
Returns
-------
:class:`~pyspark.sql.Column`
A column object representing the value from `col` that is associated with
the maximum value from `ord`.
Examples
--------
Example 1: Using `max_by` with groupBy
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([
... ("Java", 2012, 20000), ("dotNET", 2012, 5000),
... ("dotNET", 2013, 48000), ("Java", 2013, 30000)],
... schema=("course", "year", "earnings"))
>>> df.groupby("course").agg(sf.max_by("year", "earnings")).sort("course").show()
+------+----------------------+
|course|max_by(year, earnings)|
+------+----------------------+
| Java| 2013|
|dotNET| 2013|
+------+----------------------+
Example 2: Using `max_by` with different data types
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([
... ("Marketing", "Anna", 4), ("IT", "Bob", 2),
... ("IT", "Charlie", 3), ("Marketing", "David", 1)],
... schema=("department", "name", "years_in_dept"))
>>> df.groupby("department").agg(
... sf.max_by("name", "years_in_dept")
... ).sort("department").show()
+----------+---------------------------+
|department|max_by(name, years_in_dept)|
+----------+---------------------------+
| IT| Charlie|
| Marketing| Anna|
+----------+---------------------------+
Example 3: Using `max_by` where `ord` has multiple maximum values
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([
... ("Consult", "Eva", 6), ("Finance", "Frank", 5),
... ("Finance", "George", 9), ("Consult", "Henry", 7)],
... schema=("department", "name", "years_in_dept"))
>>> df.groupby("department").agg(
... sf.max_by("name", "years_in_dept")
... ).sort("department").show()
+----------+---------------------------+
|department|max_by(name, years_in_dept)|
+----------+---------------------------+
| Consult| Henry|
| Finance| George|
+----------+---------------------------+
"""
return _invoke_function_over_columns("max_by", col, ord)
@_try_remote_functions
def min_by(col: "ColumnOrName", ord: "ColumnOrName") -> Column:
"""
Returns the value from the `col` parameter that is associated with the minimum value
from the `ord` parameter. This function is often used to find the `col` parameter value
corresponding to the minimum `ord` parameter value within each group when used with groupBy().
.. versionadded:: 3.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The function is non-deterministic so the output order can be different for those
associated the same values of `col`.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The column representing the values that will be returned. This could be the column instance
or the column name as string.
ord : :class:`~pyspark.sql.Column` or column name
The column that needs to be minimized. This could be the column instance
or the column name as string.
Returns
-------
:class:`~pyspark.sql.Column`
Column object that represents the value from `col` associated with
the minimum value from `ord`.
Examples
--------
Example 1: Using `min_by` with groupBy:
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([
... ("Java", 2012, 20000), ("dotNET", 2012, 5000),
... ("dotNET", 2013, 48000), ("Java", 2013, 30000)],
... schema=("course", "year", "earnings"))
>>> df.groupby("course").agg(sf.min_by("year", "earnings")).sort("course").show()
+------+----------------------+
|course|min_by(year, earnings)|
+------+----------------------+
| Java| 2012|
|dotNET| 2012|
+------+----------------------+
Example 2: Using `min_by` with different data types:
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([
... ("Marketing", "Anna", 4), ("IT", "Bob", 2),
... ("IT", "Charlie", 3), ("Marketing", "David", 1)],
... schema=("department", "name", "years_in_dept"))
>>> df.groupby("department").agg(
... sf.min_by("name", "years_in_dept")
... ).sort("department").show()
+----------+---------------------------+
|department|min_by(name, years_in_dept)|
+----------+---------------------------+
| IT| Bob|
| Marketing| David|
+----------+---------------------------+
Example 3: Using `min_by` where `ord` has multiple minimum values:
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([
... ("Consult", "Eva", 6), ("Finance", "Frank", 5),
... ("Finance", "George", 9), ("Consult", "Henry", 7)],
... schema=("department", "name", "years_in_dept"))
>>> df.groupby("department").agg(
... sf.min_by("name", "years_in_dept")
... ).sort("department").show()
+----------+---------------------------+
|department|min_by(name, years_in_dept)|
+----------+---------------------------+
| Consult| Eva|
| Finance| Frank|
+----------+---------------------------+
"""
return _invoke_function_over_columns("min_by", col, ord)
@_try_remote_functions
def count(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the number of items in a group.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
column for computed results.
See Also
--------
:meth:`pyspark.sql.functions.count_if`
Examples
--------
Example 1: Count all rows in a DataFrame
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(None,), ("a",), ("b",), ("c",)], schema=["alphabets"])
>>> df.select(sf.count(sf.expr("*"))).show()
+--------+
|count(1)|
+--------+
| 4|
+--------+
Example 2: Count non-null values in a specific column
>>> from pyspark.sql import functions as sf
>>> df.select(sf.count(df.alphabets)).show()
+----------------+
|count(alphabets)|
+----------------+
| 3|
+----------------+
Example 3: Count all rows in a DataFrame with multiple columns
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(1, "apple"), (2, "banana"), (3, None)], schema=["id", "fruit"])
>>> df.select(sf.count(sf.expr("*"))).show()
+--------+
|count(1)|
+--------+
| 3|
+--------+
Example 4: Count non-null values in multiple columns
>>> from pyspark.sql import functions as sf
>>> df.select(sf.count(df.id), sf.count(df.fruit)).show()
+---------+------------+
|count(id)|count(fruit)|
+---------+------------+
| 3| 2|
+---------+------------+
"""
return _invoke_function_over_columns("count", col)
@_try_remote_functions
def sum(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the sum of all values in the expression.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Calculating the sum of values in a column
>>> from pyspark.sql import functions as sf
>>> df = spark.range(10)
>>> df.select(sf.sum(df["id"])).show()
+-------+
|sum(id)|
+-------+
| 45|
+-------+
Example 2: Using a plus expression together to calculate the sum
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, 2), (3, 4)], ["A", "B"])
>>> df.select(sf.sum(sf.col("A") + sf.col("B"))).show()
+------------+
|sum((A + B))|
+------------+
| 10|
+------------+
Example 3: Calculating the summation of ages with None
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1982, None), (1990, 2), (2000, 4)], ["birth", "age"])
>>> df.select(sf.sum("age")).show()
+--------+
|sum(age)|
+--------+
| 6|
+--------+
"""
return _invoke_function_over_columns("sum", col)
@_try_remote_functions
def avg(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the average of the values in a group.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Calculating the average age
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1982, 15), (1990, 2)], ["birth", "age"])
>>> df.select(sf.avg("age")).show()
+--------+
|avg(age)|
+--------+
| 8.5|
+--------+
Example 2: Calculating the average age with None
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1982, None), (1990, 2), (2000, 4)], ["birth", "age"])
>>> df.select(sf.avg("age")).show()
+--------+
|avg(age)|
+--------+
| 3.0|
+--------+
"""
return _invoke_function_over_columns("avg", col)
@_try_remote_functions
def mean(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the average of the values in a group.
An alias of :func:`avg`.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Calculating the average age
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1982, 15), (1990, 2)], ["birth", "age"])
>>> df.select(sf.mean("age")).show()
+--------+
|avg(age)|
+--------+
| 8.5|
+--------+
Example 2: Calculating the average age with None
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1982, None), (1990, 2), (2000, 4)], ["birth", "age"])
>>> df.select(sf.mean("age")).show()
+--------+
|avg(age)|
+--------+
| 3.0|
+--------+
"""
return _invoke_function_over_columns("mean", col)
@_try_remote_functions
def median(col: "ColumnOrName") -> Column:
"""
Returns the median of the values in a group.
.. versionadded:: 3.4.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the median of the values in a group.
Notes
-----
Supports Spark Connect.
:meth:`pyspark.sql.functions.percentile`
:meth:`pyspark.sql.functions.approx_percentile`
:meth:`pyspark.sql.functions.percentile_approx`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... ("Java", 2012, 20000), ("dotNET", 2012, 5000),
... ("Java", 2012, 22000), ("dotNET", 2012, 10000),
... ("dotNET", 2013, 48000), ("Java", 2013, 30000)],
... schema=("course", "year", "earnings"))
>>> df.groupby("course").agg(sf.median("earnings")).show()
+------+----------------+
|course|median(earnings)|
+------+----------------+
| Java| 22000.0|
|dotNET| 10000.0|
+------+----------------+
"""
return _invoke_function_over_columns("median", col)
@_try_remote_functions
def sumDistinct(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the sum of distinct values in the expression.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 3.2.0
Use :func:`sum_distinct` instead.
"""
warnings.warn("Deprecated in 3.2, use sum_distinct instead.", FutureWarning)
return sum_distinct(col)
@_try_remote_functions
def sum_distinct(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the sum of distinct values in the expression.
.. versionadded:: 3.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Using sum_distinct function on a column with all distinct values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,), (2,), (3,), (4,)], ["numbers"])
>>> df.select(sf.sum_distinct('numbers')).show()
+---------------------+
|sum(DISTINCT numbers)|
+---------------------+
| 10|
+---------------------+
Example 2: Using sum_distinct function on a column with no distinct values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,), (1,), (1,), (1,)], ["numbers"])
>>> df.select(sf.sum_distinct('numbers')).show()
+---------------------+
|sum(DISTINCT numbers)|
+---------------------+
| 1|
+---------------------+
Example 3: Using sum_distinct function on a column with null and duplicate values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(None,), (1,), (1,), (2,)], ["numbers"])
>>> df.select(sf.sum_distinct('numbers')).show()
+---------------------+
|sum(DISTINCT numbers)|
+---------------------+
| 3|
+---------------------+
Example 4: Using sum_distinct function on a column with all None values
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import StructType, StructField, IntegerType
>>> schema = StructType([StructField("numbers", IntegerType(), True)])
>>> df = spark.createDataFrame([(None,), (None,), (None,), (None,)], schema=schema)
>>> df.select(sf.sum_distinct('numbers')).show()
+---------------------+
|sum(DISTINCT numbers)|
+---------------------+
| NULL|
+---------------------+
"""
return _invoke_function_over_columns("sum_distinct", col)
@_try_remote_functions
def listagg(col: "ColumnOrName", delimiter: Optional[Union[Column, str, bytes]] = None) -> Column:
"""
Aggregate function: returns the concatenation of non-null input values,
separated by the delimiter.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
delimiter : :class:`~pyspark.sql.Column`, literal string or bytes, optional
the delimiter to separate the values. The default value is None.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Using listagg function
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('a',), ('b',), (None,), ('c',)], ['strings'])
>>> df.select(sf.listagg('strings')).show()
+----------------------+
|listagg(strings, NULL)|
+----------------------+
| abc|
+----------------------+
Example 2: Using listagg function with a delimiter
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('a',), ('b',), (None,), ('c',)], ['strings'])
>>> df.select(sf.listagg('strings', ', ')).show()
+--------------------+
|listagg(strings, , )|
+--------------------+
| a, b, c|
+--------------------+
Example 3: Using listagg function with a binary column and delimiter
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(b'\x01',), (b'\x02',), (None,), (b'\x03',)], ['bytes'])
>>> df.select(sf.listagg('bytes', b'\x42')).show()
+---------------------+
|listagg(bytes, X'42')|
+---------------------+
| [01 42 02 42 03]|
+---------------------+
Example 4: Using listagg function on a column with all None values
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import StructType, StructField, StringType
>>> schema = StructType([StructField("strings", StringType(), True)])
>>> df = spark.createDataFrame([(None,), (None,), (None,), (None,)], schema=schema)
>>> df.select(sf.listagg('strings')).show()
+----------------------+
|listagg(strings, NULL)|
+----------------------+
| NULL|
+----------------------+
"""
if delimiter is None:
return _invoke_function_over_columns("listagg", col)
else:
return _invoke_function_over_columns("listagg", col, lit(delimiter))
@_try_remote_functions
def listagg_distinct(
col: "ColumnOrName", delimiter: Optional[Union[Column, str, bytes]] = None
) -> Column:
"""
Aggregate function: returns the concatenation of distinct non-null input values,
separated by the delimiter.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
delimiter : :class:`~pyspark.sql.Column`, literal string or bytes, optional
the delimiter to separate the values. The default value is None.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Using listagg_distinct function
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('a',), ('b',), (None,), ('c',), ('b',)], ['strings'])
>>> df.select(sf.listagg_distinct('strings')).show()
+-------------------------------+
|listagg(DISTINCT strings, NULL)|
+-------------------------------+
| abc|
+-------------------------------+
Example 2: Using listagg_distinct function with a delimiter
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('a',), ('b',), (None,), ('c',), ('b',)], ['strings'])
>>> df.select(sf.listagg_distinct('strings', ', ')).show()
+-----------------------------+
|listagg(DISTINCT strings, , )|
+-----------------------------+
| a, b, c|
+-----------------------------+
Example 3: Using listagg_distinct function with a binary column and delimiter
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(b'\x01',), (b'\x02',), (None,), (b'\x03',), (b'\x02',)],
... ['bytes'])
>>> df.select(sf.listagg_distinct('bytes', b'\x42')).show()
+------------------------------+
|listagg(DISTINCT bytes, X'42')|
+------------------------------+
| [01 42 02 42 03]|
+------------------------------+
Example 4: Using listagg_distinct function on a column with all None values
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import StructType, StructField, StringType
>>> schema = StructType([StructField("strings", StringType(), True)])
>>> df = spark.createDataFrame([(None,), (None,), (None,), (None,)], schema=schema)
>>> df.select(sf.listagg_distinct('strings')).show()
+-------------------------------+
|listagg(DISTINCT strings, NULL)|
+-------------------------------+
| NULL|
+-------------------------------+
"""
if delimiter is None:
return _invoke_function_over_columns("listagg_distinct", col)
else:
return _invoke_function_over_columns("listagg_distinct", col, lit(delimiter))
@_try_remote_functions
def string_agg(
col: "ColumnOrName", delimiter: Optional[Union[Column, str, bytes]] = None
) -> Column:
"""
Aggregate function: returns the concatenation of non-null input values,
separated by the delimiter.
An alias of :func:`listagg`.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
delimiter : :class:`~pyspark.sql.Column`, literal string or bytes, optional
the delimiter to separate the values. The default value is None.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Using string_agg function
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('a',), ('b',), (None,), ('c',)], ['strings'])
>>> df.select(sf.string_agg('strings')).show()
+-------------------------+
|string_agg(strings, NULL)|
+-------------------------+
| abc|
+-------------------------+
Example 2: Using string_agg function with a delimiter
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('a',), ('b',), (None,), ('c',)], ['strings'])
>>> df.select(sf.string_agg('strings', ', ')).show()
+-----------------------+
|string_agg(strings, , )|
+-----------------------+
| a, b, c|
+-----------------------+
Example 3: Using string_agg function with a binary column and delimiter
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(b'\x01',), (b'\x02',), (None,), (b'\x03',)], ['bytes'])
>>> df.select(sf.string_agg('bytes', b'\x42')).show()
+------------------------+
|string_agg(bytes, X'42')|
+------------------------+
| [01 42 02 42 03]|
+------------------------+
Example 4: Using string_agg function on a column with all None values
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import StructType, StructField, StringType
>>> schema = StructType([StructField("strings", StringType(), True)])
>>> df = spark.createDataFrame([(None,), (None,), (None,), (None,)], schema=schema)
>>> df.select(sf.string_agg('strings')).show()
+-------------------------+
|string_agg(strings, NULL)|
+-------------------------+
| NULL|
+-------------------------+
"""
if delimiter is None:
return _invoke_function_over_columns("string_agg", col)
else:
return _invoke_function_over_columns("string_agg", col, lit(delimiter))
@_try_remote_functions
def string_agg_distinct(
col: "ColumnOrName", delimiter: Optional[Union[Column, str, bytes]] = None
) -> Column:
"""
Aggregate function: returns the concatenation of distinct non-null input values,
separated by the delimiter.
An alias of :func:`listagg_distinct`.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
delimiter : :class:`~pyspark.sql.Column`, literal string or bytes, optional
the delimiter to separate the values. The default value is None.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Using string_agg_distinct function
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('a',), ('b',), (None,), ('c',), ('b',)], ['strings'])
>>> df.select(sf.string_agg_distinct('strings')).show()
+----------------------------------+
|string_agg(DISTINCT strings, NULL)|
+----------------------------------+
| abc|
+----------------------------------+
Example 2: Using string_agg_distinct function with a delimiter
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('a',), ('b',), (None,), ('c',), ('b',)], ['strings'])
>>> df.select(sf.string_agg_distinct('strings', ', ')).show()
+--------------------------------+
|string_agg(DISTINCT strings, , )|
+--------------------------------+
| a, b, c|
+--------------------------------+
Example 3: Using string_agg_distinct function with a binary column and delimiter
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(b'\x01',), (b'\x02',), (None,), (b'\x03',), (b'\x02',)],
... ['bytes'])
>>> df.select(sf.string_agg_distinct('bytes', b'\x42')).show()
+---------------------------------+
|string_agg(DISTINCT bytes, X'42')|
+---------------------------------+
| [01 42 02 42 03]|
+---------------------------------+
Example 4: Using string_agg_distinct function on a column with all None values
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import StructType, StructField, StringType
>>> schema = StructType([StructField("strings", StringType(), True)])
>>> df = spark.createDataFrame([(None,), (None,), (None,), (None,)], schema=schema)
>>> df.select(sf.string_agg_distinct('strings')).show()
+----------------------------------+
|string_agg(DISTINCT strings, NULL)|
+----------------------------------+
| NULL|
+----------------------------------+
"""
if delimiter is None:
return _invoke_function_over_columns("string_agg_distinct", col)
else:
return _invoke_function_over_columns("string_agg_distinct", col, lit(delimiter))
@_try_remote_functions
def product(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the product of the values in a group.
.. versionadded:: 3.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column containing values to be multiplied together
Returns
-------
:class:`~pyspark.sql.Column` or column name
the column for computed results.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT id % 3 AS mod3, id AS value FROM RANGE(10)")
>>> df.groupBy('mod3').agg(sf.product('value')).orderBy('mod3').show()
+----+--------------+
|mod3|product(value)|
+----+--------------+
| 0| 0.0|
| 1| 28.0|
| 2| 80.0|
+----+--------------+
"""
return _invoke_function_over_columns("product", col)
@_try_remote_functions
def acos(col: "ColumnOrName") -> Column:
"""
Mathematical Function: Computes the inverse cosine (also known as arccosine)
of the given column or expression.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column or expression to compute the inverse cosine on.
Returns
-------
:class:`~pyspark.sql.Column`
A new column object representing the inverse cosine of the input.
Examples
--------
Example 1: Compute the inverse cosine
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-1.0,), (-0.5,), (0.0,), (0.5,), (1.0,)], ["value"])
>>> df.select("*", sf.acos("value")).show()
+-----+------------------+
|value| ACOS(value)|
+-----+------------------+
| -1.0| 3.141592653589...|
| -0.5|2.0943951023931...|
| 0.0|1.5707963267948...|
| 0.5|1.0471975511965...|
| 1.0| 0.0|
+-----+------------------+
Example 2: Compute the inverse cosine of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (-2), (2), (NULL) AS TAB(value)"
... ).select("*", sf.acos("value")).show()
+-----+-----------+
|value|ACOS(value)|
+-----+-----------+
| -2| NaN|
| 2| NaN|
| NULL| NULL|
+-----+-----------+
"""
return _invoke_function_over_columns("acos", col)
@_try_remote_functions
def acosh(col: "ColumnOrName") -> Column:
"""
Mathematical Function: Computes the inverse hyperbolic cosine (also known as arcosh)
of the given column or expression.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column or expression to compute the inverse hyperbolic cosine on.
Returns
-------
:class:`~pyspark.sql.Column`
A new column object representing the inverse hyperbolic cosine of the input.
Examples
--------
Example 1: Compute the inverse hyperbolic cosine
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,), (2,)], ["value"])
>>> df.select("*", sf.acosh(df.value)).show()
+-----+------------------+
|value| ACOSH(value)|
+-----+------------------+
| 1| 0.0|
| 2|1.3169578969248...|
+-----+------------------+
Example 2: Compute the inverse hyperbolic cosine of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (-0.5), (0.5), (NULL) AS TAB(value)"
... ).select("*", sf.acosh("value")).show()
+-----+------------+
|value|ACOSH(value)|
+-----+------------+
| -0.5| NaN|
| 0.5| NaN|
| NULL| NULL|
+-----+------------+
"""
return _invoke_function_over_columns("acosh", col)
@_try_remote_functions
def asin(col: "ColumnOrName") -> Column:
"""
Computes inverse sine of the input column.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
inverse sine of `col`, as if computed by `java.lang.Math.asin()`
Examples
--------
Example 1: Compute the inverse sine
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-0.5,), (0.0,), (0.5,)], ["value"])
>>> df.select("*", sf.asin(df.value)).show()
+-----+-------------------+
|value| ASIN(value)|
+-----+-------------------+
| -0.5|-0.5235987755982...|
| 0.0| 0.0|
| 0.5| 0.5235987755982...|
+-----+-------------------+
Example 2: Compute the inverse sine of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (-2), (2), (NULL) AS TAB(value)"
... ).select("*", sf.asin("value")).show()
+-----+-----------+
|value|ASIN(value)|
+-----+-----------+
| -2| NaN|
| 2| NaN|
| NULL| NULL|
+-----+-----------+
"""
return _invoke_function_over_columns("asin", col)
@_try_remote_functions
def asinh(col: "ColumnOrName") -> Column:
"""
Computes inverse hyperbolic sine of the input column.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Compute the inverse hyperbolic sine
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-0.5,), (0.0,), (0.5,)], ["value"])
>>> df.select("*", sf.asinh(df.value)).show()
+-----+--------------------+
|value| ASINH(value)|
+-----+--------------------+
| -0.5|-0.48121182505960...|
| 0.0| 0.0|
| 0.5| 0.48121182505960...|
+-----+--------------------+
Example 2: Compute the inverse hyperbolic sine of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.asinh("value")).show()
+-----+------------+
|value|ASINH(value)|
+-----+------------+
| NaN| NaN|
| NULL| NULL|
+-----+------------+
"""
return _invoke_function_over_columns("asinh", col)
@_try_remote_functions
def atan(col: "ColumnOrName") -> Column:
"""
Compute inverse tangent of the input column.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
inverse tangent of `col`, as if computed by `java.lang.Math.atan()`
Examples
--------
Example 1: Compute the inverse tangent
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-0.5,), (0.0,), (0.5,)], ["value"])
>>> df.select("*", sf.atan(df.value)).show()
+-----+-------------------+
|value| ATAN(value)|
+-----+-------------------+
| -0.5|-0.4636476090008...|
| 0.0| 0.0|
| 0.5| 0.4636476090008...|
+-----+-------------------+
Example 2: Compute the inverse tangent of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.atan("value")).show()
+-----+-----------+
|value|ATAN(value)|
+-----+-----------+
| NaN| NaN|
| NULL| NULL|
+-----+-----------+
"""
return _invoke_function_over_columns("atan", col)
@_try_remote_functions
def atanh(col: "ColumnOrName") -> Column:
"""
Computes inverse hyperbolic tangent of the input column.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Compute the inverse hyperbolic tangent
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-0.5,), (0.0,), (0.5,)], ["value"])
>>> df.select("*", sf.atanh(df.value)).show()
+-----+-------------------+
|value| ATANH(value)|
+-----+-------------------+
| -0.5|-0.5493061443340...|
| 0.0| 0.0|
| 0.5| 0.5493061443340...|
+-----+-------------------+
Example 2: Compute the inverse hyperbolic tangent of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (-2), (2), (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.atanh("value")).show()
+-----+------------+
|value|ATANH(value)|
+-----+------------+
| -2.0| NaN|
| 2.0| NaN|
| NaN| NaN|
| NULL| NULL|
+-----+------------+
"""
return _invoke_function_over_columns("atanh", col)
@_try_remote_functions
def cbrt(col: "ColumnOrName") -> Column:
"""
Computes the cube-root of the given value.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
Example 1: Compute the cube-root
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-8,), (0,), (8,)], ["value"])
>>> df.select("*", sf.cbrt(df.value)).show()
+-----+-----------+
|value|CBRT(value)|
+-----+-----------+
| -8| -2.0|
| 0| 0.0|
| 8| 2.0|
+-----+-----------+
Example 2: Compute the cube-root of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.cbrt("value")).show()
+-----+-----------+
|value|CBRT(value)|
+-----+-----------+
| NaN| NaN|
| NULL| NULL|
+-----+-----------+
"""
return _invoke_function_over_columns("cbrt", col)
@_try_remote_functions
def ceil(col: "ColumnOrName", scale: Optional[Union[Column, int]] = None) -> Column:
"""
Computes the ceiling of the given value.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column or column name to compute the ceiling on.
scale : :class:`~pyspark.sql.Column` or int, optional
An optional parameter to control the rounding behavior.
.. versionadded:: 4.0.0
Returns
-------
:class:`~pyspark.sql.Column`
A column for the computed results.
Examples
--------
Example 1: Compute the ceiling of a column value
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.ceil(sf.lit(-0.1))).show()
+----------+
|CEIL(-0.1)|
+----------+
| 0|
+----------+
Example 2: Compute the ceiling of a column value with a specified scale
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.ceil(sf.lit(-0.1), 1)).show()
+-------------+
|ceil(-0.1, 1)|
+-------------+
| -0.1|
+-------------+
"""
if scale is None:
return _invoke_function_over_columns("ceil", col)
else:
scale = _enum_to_value(scale)
scale = lit(scale) if isinstance(scale, int) else scale
return _invoke_function_over_columns("ceil", col, scale) # type: ignore[arg-type]
@_try_remote_functions
def ceiling(col: "ColumnOrName", scale: Optional[Union[Column, int]] = None) -> Column:
"""
Computes the ceiling of the given value.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column or column name to compute the ceiling on.
scale : :class:`~pyspark.sql.Column` or int
An optional parameter to control the rounding behavior.
.. versionadded:: 4.0.0
Returns
-------
:class:`~pyspark.sql.Column`
A column for the computed results.
Examples
--------
Example 1: Compute the ceiling of a column value
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.ceiling(sf.lit(-0.1))).show()
+-------------+
|ceiling(-0.1)|
+-------------+
| 0|
+-------------+
Example 2: Compute the ceiling of a column value with a specified scale
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.ceiling(sf.lit(-0.1), 1)).show()
+----------------+
|ceiling(-0.1, 1)|
+----------------+
| -0.1|
+----------------+
"""
if scale is None:
return _invoke_function_over_columns("ceiling", col)
else:
scale = _enum_to_value(scale)
scale = lit(scale) if isinstance(scale, int) else scale
return _invoke_function_over_columns("ceiling", col, scale) # type: ignore[arg-type]
@_try_remote_functions
def cos(col: "ColumnOrName") -> Column:
"""
Computes cosine of the input column.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
angle in radians
Returns
-------
:class:`~pyspark.sql.Column`
cosine of the angle, as if computed by `java.lang.Math.cos()`.
Examples
--------
Example 1: Compute the cosine
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (PI()), (PI() / 4), (PI() / 16) AS TAB(value)"
... ).select("*", sf.cos("value")).show()
+-------------------+------------------+
| value| COS(value)|
+-------------------+------------------+
| 3.141592653589...| -1.0|
| 0.7853981633974...|0.7071067811865...|
|0.19634954084936...|0.9807852804032...|
+-------------------+------------------+
Example 2: Compute the cosine of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.cos("value")).show()
+-----+----------+
|value|COS(value)|
+-----+----------+
| NaN| NaN|
| NULL| NULL|
+-----+----------+
"""
return _invoke_function_over_columns("cos", col)
@_try_remote_functions
def cosh(col: "ColumnOrName") -> Column:
"""
Computes hyperbolic cosine of the input column.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
hyperbolic angle
Returns
-------
:class:`~pyspark.sql.Column`
hyperbolic cosine of the angle, as if computed by `java.lang.Math.cosh()`
Examples
--------
Example 1: Compute the cosine
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-1,), (0,), (1,)], ["value"])
>>> df.select("*", sf.cosh(df.value)).show()
+-----+-----------------+
|value| COSH(value)|
+-----+-----------------+
| -1|1.543080634815...|
| 0| 1.0|
| 1|1.543080634815...|
+-----+-----------------+
Example 2: Compute the cosine of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.cosh("value")).show()
+-----+-----------+
|value|COSH(value)|
+-----+-----------+
| NaN| NaN|
| NULL| NULL|
+-----+-----------+
"""
return _invoke_function_over_columns("cosh", col)
@_try_remote_functions
def cot(col: "ColumnOrName") -> Column:
"""
Computes cotangent of the input column.
.. versionadded:: 3.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
angle in radians.
Returns
-------
:class:`~pyspark.sql.Column`
cotangent of the angle.
Examples
--------
Example 1: Compute the cotangent
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (PI() / 4), (PI() / 16) AS TAB(value)"
... ).select("*", sf.cot("value")).show()
+-------------------+------------------+
| value| COT(value)|
+-------------------+------------------+
| 0.7853981633974...|1.0000000000000...|
|0.19634954084936...| 5.027339492125...|
+-------------------+------------------+
Example 2: Compute the cotangent of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (0.0), (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.cot("value")).show()
+-----+----------+
|value|COT(value)|
+-----+----------+
| 0.0| Infinity|
| NaN| NaN|
| NULL| NULL|
+-----+----------+
"""
return _invoke_function_over_columns("cot", col)
@_try_remote_functions
def csc(col: "ColumnOrName") -> Column:
"""
Computes cosecant of the input column.
.. versionadded:: 3.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
angle in radians.
Returns
-------
:class:`~pyspark.sql.Column`
cosecant of the angle.
Examples
--------
Example 1: Compute the cosecant
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (PI() / 2), (PI() / 4) AS TAB(value)"
... ).select("*", sf.csc("value")).show()
+------------------+------------------+
| value| CSC(value)|
+------------------+------------------+
|1.5707963267948...| 1.0|
|0.7853981633974...|1.4142135623730...|
+------------------+------------------+
Example 2: Compute the cosecant of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (0.0), (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.csc("value")).show()
+-----+----------+
|value|CSC(value)|
+-----+----------+
| 0.0| Infinity|
| NaN| NaN|
| NULL| NULL|
+-----+----------+
"""
return _invoke_function_over_columns("csc", col)
@_try_remote_functions
def e() -> Column:
"""Returns Euler's number.
.. versionadded:: 3.5.0
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.e()).show()
+-----------------+
| E()|
+-----------------+
|2.718281828459045|
+-----------------+
"""
return _invoke_function("e")
@_try_remote_functions
def exp(col: "ColumnOrName") -> Column:
"""
Computes the exponential of the given value.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to calculate exponential for.
Returns
-------
:class:`~pyspark.sql.Column`
exponential of the given value.
Examples
--------
Example 1: Compute the exponential
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT id AS value FROM RANGE(5)")
>>> df.select("*", sf.exp(df.value)).show()
+-----+------------------+
|value| EXP(value)|
+-----+------------------+
| 0| 1.0|
| 1|2.7182818284590...|
| 2| 7.38905609893...|
| 3|20.085536923187...|
| 4|54.598150033144...|
+-----+------------------+
Example 2: Compute the exponential of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.exp("value")).show()
+-----+----------+
|value|EXP(value)|
+-----+----------+
| NaN| NaN|
| NULL| NULL|
+-----+----------+
"""
return _invoke_function_over_columns("exp", col)
@_try_remote_functions
def expm1(col: "ColumnOrName") -> Column:
"""
Computes the exponential of the given value minus one.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to calculate exponential for.
Returns
-------
:class:`~pyspark.sql.Column`
exponential less one.
Examples
--------
Example 1: Compute the exponential minus one
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT id AS value FROM RANGE(5)")
>>> df.select("*", sf.expm1(df.value)).show()
+-----+------------------+
|value| EXPM1(value)|
+-----+------------------+
| 0| 0.0|
| 1| 1.718281828459...|
| 2| 6.38905609893...|
| 3|19.085536923187...|
| 4|53.598150033144...|
+-----+------------------+
Example 2: Compute the exponential minus one of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.expm1("value")).show()
+-----+------------+
|value|EXPM1(value)|
+-----+------------+
| NaN| NaN|
| NULL| NULL|
+-----+------------+
"""
return _invoke_function_over_columns("expm1", col)
@_try_remote_functions
def floor(col: "ColumnOrName", scale: Optional[Union[Column, int]] = None) -> Column:
"""
Computes the floor of the given value.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column or column name to compute the floor on.
scale : :class:`~pyspark.sql.Column` or int, optional
An optional parameter to control the rounding behavior.
.. versionadded:: 4.0.0
Returns
-------
:class:`~pyspark.sql.Column`
nearest integer that is less than or equal to given value.
Examples
--------
Example 1: Compute the floor of a column value
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.floor(sf.lit(2.5))).show()
+----------+
|FLOOR(2.5)|
+----------+
| 2|
+----------+
Example 2: Compute the floor of a column value with a specified scale
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.floor(sf.lit(2.1267), sf.lit(2))).show()
+----------------+
|floor(2.1267, 2)|
+----------------+
| 2.12|
+----------------+
"""
if scale is None:
return _invoke_function_over_columns("floor", col)
else:
scale = _enum_to_value(scale)
scale = lit(scale) if isinstance(scale, int) else scale
return _invoke_function_over_columns("floor", col, scale) # type: ignore[arg-type]
@_try_remote_functions
def log(col: "ColumnOrName") -> Column:
"""
Computes the natural logarithm of the given value.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to calculate natural logarithm for.
Returns
-------
:class:`~pyspark.sql.Column`
natural logarithm of the given value.
Examples
--------
Example 1: Compute the natural logarithm of E
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.log(sf.e())).show()
+-------+
|ln(E())|
+-------+
| 1.0|
+-------+
Example 2: Compute the natural logarithm of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (-1), (0), (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.log("value")).show()
+-----+---------+
|value|ln(value)|
+-----+---------+
| -1.0| NULL|
| 0.0| NULL|
| NaN| NaN|
| NULL| NULL|
+-----+---------+
"""
return _invoke_function_over_columns("log", col)
@_try_remote_functions
def log10(col: "ColumnOrName") -> Column:
"""
Computes the logarithm of the given value in Base 10.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to calculate logarithm for.
Returns
-------
:class:`~pyspark.sql.Column`
logarithm of the given value in Base 10.
Examples
--------
Example 1: Compute the logarithm in Base 10
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,), (10,), (100,)], ["value"])
>>> df.select("*", sf.log10(df.value)).show()
+-----+------------+
|value|LOG10(value)|
+-----+------------+
| 1| 0.0|
| 10| 1.0|
| 100| 2.0|
+-----+------------+
Example 2: Compute the logarithm in Base 10 of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (-1), (0), (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.log10("value")).show()
+-----+------------+
|value|LOG10(value)|
+-----+------------+
| -1.0| NULL|
| 0.0| NULL|
| NaN| NaN|
| NULL| NULL|
+-----+------------+
"""
return _invoke_function_over_columns("log10", col)
@_try_remote_functions
def log1p(col: "ColumnOrName") -> Column:
"""
Computes the natural logarithm of the given value plus one.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to calculate natural logarithm for.
Returns
-------
:class:`~pyspark.sql.Column`
natural logarithm of the "given value plus one".
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.log1p(sf.e())).show()
+------------------+
| LOG1P(E())|
+------------------+
|1.3132616875182...|
+------------------+
Same as:
>>> spark.range(1).select(sf.log(sf.e() + 1)).show()
+------------------+
| ln((E() + 1))|
+------------------+
|1.3132616875182...|
+------------------+
"""
return _invoke_function_over_columns("log1p", col)
@_try_remote_functions
def negative(col: "ColumnOrName") -> Column:
"""
Returns the negative value.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to calculate negative value for.
Returns
-------
:class:`~pyspark.sql.Column`
negative value.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(-1,), (0,), (1,)], ["value"])
>>> df.select("*", sf.negative(df.value)).show()
+-----+---------------+
|value|negative(value)|
+-----+---------------+
| -1| 1|
| 0| 0|
| 1| -1|
+-----+---------------+
"""
return _invoke_function_over_columns("negative", col)
negate = negative
@_try_remote_functions
def pi() -> Column:
"""Returns Pi.
.. versionadded:: 3.5.0
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.pi()).show()
+-----------------+
| PI()|
+-----------------+
|3.141592653589793|
+-----------------+
"""
return _invoke_function("pi")
@_try_remote_functions
def positive(col: "ColumnOrName") -> Column:
"""
Returns the value.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input value column.
Returns
-------
:class:`~pyspark.sql.Column`
value.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(-1,), (0,), (1,)], ["value"])
>>> df.select("*", sf.positive(df.value)).show()
+-----+---------+
|value|(+ value)|
+-----+---------+
| -1| -1|
| 0| 0|
| 1| 1|
+-----+---------+
"""
return _invoke_function_over_columns("positive", col)
@_try_remote_functions
def rint(col: "ColumnOrName") -> Column:
"""
Returns the double value that is closest in value to the argument and
is equal to a mathematical integer.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.rint(sf.lit(10.6))).show()
+----------+
|rint(10.6)|
+----------+
| 11.0|
+----------+
>>> spark.range(1).select(sf.rint(sf.lit(10.3))).show()
+----------+
|rint(10.3)|
+----------+
| 10.0|
+----------+
"""
return _invoke_function_over_columns("rint", col)
@_try_remote_functions
def sec(col: "ColumnOrName") -> Column:
"""
Computes secant of the input column.
.. versionadded:: 3.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Angle in radians
Returns
-------
:class:`~pyspark.sql.Column`
Secant of the angle.
Examples
--------
Example 1: Compute the secant
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (PI() / 4), (PI() / 16) AS TAB(value)"
... ).select("*", sf.sec("value")).show()
+-------------------+------------------+
| value| SEC(value)|
+-------------------+------------------+
| 0.7853981633974...| 1.414213562373...|
|0.19634954084936...|1.0195911582083...|
+-------------------+------------------+
Example 2: Compute the secant of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.sec("value")).show()
+-----+----------+
|value|SEC(value)|
+-----+----------+
| NaN| NaN|
| NULL| NULL|
+-----+----------+
"""
return _invoke_function_over_columns("sec", col)
@_try_remote_functions
def signum(col: "ColumnOrName") -> Column:
"""
Computes the signum of the given value.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(
... sf.signum(sf.lit(-5)),
... sf.signum(sf.lit(6)),
... sf.signum(sf.lit(float('nan'))),
... sf.signum(sf.lit(None))
... ).show()
+----------+---------+-----------+------------+
|SIGNUM(-5)|SIGNUM(6)|SIGNUM(NaN)|SIGNUM(NULL)|
+----------+---------+-----------+------------+
| -1.0| 1.0| NaN| NULL|
+----------+---------+-----------+------------+
"""
return _invoke_function_over_columns("signum", col)
@_try_remote_functions
def sign(col: "ColumnOrName") -> Column:
"""
Computes the signum of the given value.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(
... sf.sign(sf.lit(-5)),
... sf.sign(sf.lit(6)),
... sf.sign(sf.lit(float('nan'))),
... sf.sign(sf.lit(None))
... ).show()
+--------+-------+---------+----------+
|sign(-5)|sign(6)|sign(NaN)|sign(NULL)|
+--------+-------+---------+----------+
| -1.0| 1.0| NaN| NULL|
+--------+-------+---------+----------+
"""
return _invoke_function_over_columns("sign", col)
@_try_remote_functions
def sin(col: "ColumnOrName") -> Column:
"""
Computes sine of the input column.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
sine of the angle, as if computed by `java.lang.Math.sin()`
Examples
--------
Example 1: Compute the sine
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (0.0), (PI() / 2), (PI() / 4) AS TAB(value)"
... ).select("*", sf.sin("value")).show()
+------------------+------------------+
| value| SIN(value)|
+------------------+------------------+
| 0.0| 0.0|
|1.5707963267948...| 1.0|
|0.7853981633974...|0.7071067811865...|
+------------------+------------------+
Example 2: Compute the sine of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.sin("value")).show()
+-----+----------+
|value|SIN(value)|
+-----+----------+
| NaN| NaN|
| NULL| NULL|
+-----+----------+
"""
return _invoke_function_over_columns("sin", col)
@_try_remote_functions
def sinh(col: "ColumnOrName") -> Column:
"""
Computes hyperbolic sine of the input column.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
hyperbolic angle.
Returns
-------
:class:`~pyspark.sql.Column`
hyperbolic sine of the given value,
as if computed by `java.lang.Math.sinh()`
Examples
--------
Example 1: Compute the hyperbolic sine
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-1,), (0,), (1,)], ["value"])
>>> df.select("*", sf.sinh(df.value)).show()
+-----+-------------------+
|value| SINH(value)|
+-----+-------------------+
| -1|-1.1752011936438...|
| 0| 0.0|
| 1| 1.1752011936438...|
+-----+-------------------+
Example 2: Compute the hyperbolic sine of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.sinh("value")).show()
+-----+-----------+
|value|SINH(value)|
+-----+-----------+
| NaN| NaN|
| NULL| NULL|
+-----+-----------+
"""
return _invoke_function_over_columns("sinh", col)
@_try_remote_functions
def tan(col: "ColumnOrName") -> Column:
"""
Computes tangent of the input column.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
angle in radians
Returns
-------
:class:`~pyspark.sql.Column`
tangent of the given value, as if computed by `java.lang.Math.tan()`
Examples
--------
Example 1: Compute the tangent
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (0.0), (PI() / 4), (PI() / 6) AS TAB(value)"
... ).select("*", sf.tan("value")).show()
+------------------+------------------+
| value| TAN(value)|
+------------------+------------------+
| 0.0| 0.0|
|0.7853981633974...|0.9999999999999...|
|0.5235987755982...|0.5773502691896...|
+------------------+------------------+
Example 2: Compute the tangent of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.tan("value")).show()
+-----+----------+
|value|TAN(value)|
+-----+----------+
| NaN| NaN|
| NULL| NULL|
+-----+----------+
"""
return _invoke_function_over_columns("tan", col)
@_try_remote_functions
def tanh(col: "ColumnOrName") -> Column:
"""
Computes hyperbolic tangent of the input column.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
hyperbolic angle
Returns
-------
:class:`~pyspark.sql.Column`
hyperbolic tangent of the given value
as if computed by `java.lang.Math.tanh()`
Examples
--------
Example 1: Compute the hyperbolic tangent sine
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(-1,), (0,), (1,)], ["value"])
>>> df.select("*", sf.tanh(df.value)).show()
+-----+-------------------+
|value| TANH(value)|
+-----+-------------------+
| -1|-0.7615941559557...|
| 0| 0.0|
| 1| 0.7615941559557...|
+-----+-------------------+
Example 2: Compute the hyperbolic tangent of invalid values
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (FLOAT('NAN')), (NULL) AS TAB(value)"
... ).select("*", sf.tanh("value")).show()
+-----+-----------+
|value|TANH(value)|
+-----+-----------+
| NaN| NaN|
| NULL| NULL|
+-----+-----------+
"""
return _invoke_function_over_columns("tanh", col)
@_try_remote_functions
def toDegrees(col: "ColumnOrName") -> Column:
"""
Converts an angle measured in radians to an approximately equivalent angle
measured in degrees.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 2.1.0
Use :func:`degrees` instead.
"""
warnings.warn("Deprecated in 2.1, use degrees instead.", FutureWarning)
return degrees(col)
@_try_remote_functions
def toRadians(col: "ColumnOrName") -> Column:
"""
Converts an angle measured in degrees to an approximately equivalent angle
measured in radians.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 2.1.0
Use :func:`radians` instead.
"""
warnings.warn("Deprecated in 2.1, use radians instead.", FutureWarning)
return radians(col)
@_try_remote_functions
def bitwiseNOT(col: "ColumnOrName") -> Column:
"""
Computes bitwise not.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 3.2.0
Use :func:`bitwise_not` instead.
"""
warnings.warn("Deprecated in 3.2, use bitwise_not instead.", FutureWarning)
return bitwise_not(col)
@_try_remote_functions
def bitwise_not(col: "ColumnOrName") -> Column:
"""
Computes bitwise not.
.. versionadded:: 3.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (0), (1), (2), (3), (NULL) AS TAB(value)"
... ).select("*", sf.bitwise_not("value")).show()
+-----+------+
|value|~value|
+-----+------+
| 0| -1|
| 1| -2|
| 2| -3|
| 3| -4|
| NULL| NULL|
+-----+------+
"""
return _invoke_function_over_columns("bitwise_not", col)
@_try_remote_functions
def bit_count(col: "ColumnOrName") -> Column:
"""
Returns the number of bits that are set in the argument expr as an unsigned 64-bit integer,
or NULL if the argument is NULL.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the number of bits that are set in the argument expr as an unsigned 64-bit integer,
or NULL if the argument is NULL.
See Also
--------
:meth:`pyspark.sql.functions.bit_get`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (0), (1), (2), (3), (NULL) AS TAB(value)"
... ).select("*", sf.bit_count("value")).show()
+-----+----------------+
|value|bit_count(value)|
+-----+----------------+
| 0| 0|
| 1| 1|
| 2| 1|
| 3| 2|
| NULL| NULL|
+-----+----------------+
"""
return _invoke_function_over_columns("bit_count", col)
@_try_remote_functions
def bit_get(col: "ColumnOrName", pos: "ColumnOrName") -> Column:
"""
Returns the value of the bit (0 or 1) at the specified position.
The positions are numbered from right to left, starting at zero.
The position argument cannot be negative.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
pos : :class:`~pyspark.sql.Column` or column name
The positions are numbered from right to left, starting at zero.
Returns
-------
:class:`~pyspark.sql.Column`
the value of the bit (0 or 1) at the specified position.
See Also
--------
:meth:`pyspark.sql.functions.bit_count`
:meth:`pyspark.sql.functions.getbit`
Examples
--------
Example 1: Get the bit with a literal position
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[2],[3],[None]], ["value"])
>>> df.select("*", sf.bit_get("value", sf.lit(1))).show()
+-----+-----------------+
|value|bit_get(value, 1)|
+-----+-----------------+
| 1| 0|
| 2| 1|
| 3| 1|
| NULL| NULL|
+-----+-----------------+
Example 2: Get the bit with a column position
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1,2],[2,1],[3,None],[None,1]], ["value", "pos"])
>>> df.select("*", sf.bit_get(df.value, "pos")).show()
+-----+----+-------------------+
|value| pos|bit_get(value, pos)|
+-----+----+-------------------+
| 1| 2| 0|
| 2| 1| 1|
| 3|NULL| NULL|
| NULL| 1| NULL|
+-----+----+-------------------+
"""
return _invoke_function_over_columns("bit_get", col, pos)
@_try_remote_functions
def getbit(col: "ColumnOrName", pos: "ColumnOrName") -> Column:
"""
Returns the value of the bit (0 or 1) at the specified position.
The positions are numbered from right to left, starting at zero.
The position argument cannot be negative.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
pos : :class:`~pyspark.sql.Column` or column name
The positions are numbered from right to left, starting at zero.
Returns
-------
:class:`~pyspark.sql.Column`
the value of the bit (0 or 1) at the specified position.
See Also
--------
:meth:`pyspark.sql.functions.bit_get`
Examples
--------
Example 1: Get the bit with a literal position
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [[1], [2], [3], [None]], ["value"]
... ).select("*", sf.getbit("value", sf.lit(1))).show()
+-----+----------------+
|value|getbit(value, 1)|
+-----+----------------+
| 1| 0|
| 2| 1|
| 3| 1|
| NULL| NULL|
+-----+----------------+
Example 2: Get the bit with a column position
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1,2],[2,1],[3,None],[None,1]], ["value", "pos"])
>>> df.select("*", sf.getbit(df.value, "pos")).show()
+-----+----+------------------+
|value| pos|getbit(value, pos)|
+-----+----+------------------+
| 1| 2| 0|
| 2| 1| 1|
| 3|NULL| NULL|
| NULL| 1| NULL|
+-----+----+------------------+
"""
return _invoke_function_over_columns("getbit", col, pos)
@_try_remote_functions
def asc_nulls_first(col: "ColumnOrName") -> Column:
"""
Sort Function: Returns a sort expression based on the ascending order of the given
column name, and null values return before non-null values.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to sort by in the ascending order.
Returns
-------
:class:`~pyspark.sql.Column`
the column specifying the order.
See Also
--------
:meth:`pyspark.sql.functions.asc`
:meth:`pyspark.sql.functions.asc_nulls_last`
Examples
--------
Example 1: Sorting a DataFrame with null values in ascending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, "Bob"), (0, None), (2, "Alice")], ["age", "name"])
>>> df.sort(sf.asc_nulls_first(df.name)).show()
+---+-----+
|age| name|
+---+-----+
| 0| NULL|
| 2|Alice|
| 1| Bob|
+---+-----+
Example 2: Sorting a DataFrame with multiple columns, null values in ascending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(1, "Bob", None), (0, None, "Z"), (2, "Alice", "Y")], ["age", "name", "grade"])
>>> df.sort(sf.asc_nulls_first(df.name), sf.asc_nulls_first(df.grade)).show()
+---+-----+-----+
|age| name|grade|
+---+-----+-----+
| 0| NULL| Z|
| 2|Alice| Y|
| 1| Bob| NULL|
+---+-----+-----+
Example 3: Sorting a DataFrame with null values in ascending order using column name string
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, "Bob"), (0, None), (2, "Alice")], ["age", "name"])
>>> df.sort(sf.asc_nulls_first("name")).show()
+---+-----+
|age| name|
+---+-----+
| 0| NULL|
| 2|Alice|
| 1| Bob|
+---+-----+
"""
return (
col.asc_nulls_first()
if isinstance(col, Column)
else _invoke_function("asc_nulls_first", col)
)
@_try_remote_functions
def asc_nulls_last(col: "ColumnOrName") -> Column:
"""
Sort Function: Returns a sort expression based on the ascending order of the given
column name, and null values appear after non-null values.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to sort by in the ascending order.
Returns
-------
:class:`~pyspark.sql.Column`
the column specifying the order.
See Also
--------
:meth:`pyspark.sql.functions.asc`
:meth:`pyspark.sql.functions.asc_nulls_first`
Examples
--------
Example 1: Sorting a DataFrame with null values in ascending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(0, None), (1, "Bob"), (2, "Alice")], ["age", "name"])
>>> df.sort(sf.asc_nulls_last(df.name)).show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 1| Bob|
| 0| NULL|
+---+-----+
Example 2: Sorting a DataFrame with multiple columns, null values in ascending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(0, None, "Z"), (1, "Bob", None), (2, "Alice", "Y")], ["age", "name", "grade"])
>>> df.sort(sf.asc_nulls_last(df.name), sf.asc_nulls_last(df.grade)).show()
+---+-----+-----+
|age| name|grade|
+---+-----+-----+
| 2|Alice| Y|
| 1| Bob| NULL|
| 0| NULL| Z|
+---+-----+-----+
Example 3: Sorting a DataFrame with null values in ascending order using column name string
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(0, None), (1, "Bob"), (2, "Alice")], ["age", "name"])
>>> df.sort(sf.asc_nulls_last("name")).show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 1| Bob|
| 0| NULL|
+---+-----+
"""
return (
col.asc_nulls_last() if isinstance(col, Column) else _invoke_function("asc_nulls_last", col)
)
@_try_remote_functions
def desc_nulls_first(col: "ColumnOrName") -> Column:
"""
Sort Function: Returns a sort expression based on the descending order of the given
column name, and null values appear before non-null values.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to sort by in the descending order.
Returns
-------
:class:`~pyspark.sql.Column`
the column specifying the order.
See Also
--------
:meth:`pyspark.sql.functions.desc`
:meth:`pyspark.sql.functions.desc_nulls_last`
Examples
--------
Example 1: Sorting a DataFrame with null values in descending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, "Bob"), (0, None), (2, "Alice")], ["age", "name"])
>>> df.sort(sf.desc_nulls_first(df.name)).show()
+---+-----+
|age| name|
+---+-----+
| 0| NULL|
| 1| Bob|
| 2|Alice|
+---+-----+
Example 2: Sorting a DataFrame with multiple columns, null values in descending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(1, "Bob", None), (0, None, "Z"), (2, "Alice", "Y")], ["age", "name", "grade"])
>>> df.sort(sf.desc_nulls_first(df.name), sf.desc_nulls_first(df.grade)).show()
+---+-----+-----+
|age| name|grade|
+---+-----+-----+
| 0| NULL| Z|
| 1| Bob| NULL|
| 2|Alice| Y|
+---+-----+-----+
Example 3: Sorting a DataFrame with null values in descending order using column name string
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, "Bob"), (0, None), (2, "Alice")], ["age", "name"])
>>> df.sort(sf.desc_nulls_first("name")).show()
+---+-----+
|age| name|
+---+-----+
| 0| NULL|
| 1| Bob|
| 2|Alice|
+---+-----+
"""
return (
col.desc_nulls_first()
if isinstance(col, Column)
else _invoke_function("desc_nulls_first", col)
)
@_try_remote_functions
def desc_nulls_last(col: "ColumnOrName") -> Column:
"""
Sort Function: Returns a sort expression based on the descending order of the given
column name, and null values appear after non-null values.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to sort by in the descending order.
Returns
-------
:class:`~pyspark.sql.Column`
the column specifying the order.
See Also
--------
:meth:`pyspark.sql.functions.desc`
:meth:`pyspark.sql.functions.desc_nulls_first`
Examples
--------
Example 1: Sorting a DataFrame with null values in descending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(0, None), (1, "Bob"), (2, "Alice")], ["age", "name"])
>>> df.sort(sf.desc_nulls_last(df.name)).show()
+---+-----+
|age| name|
+---+-----+
| 1| Bob|
| 2|Alice|
| 0| NULL|
+---+-----+
Example 2: Sorting a DataFrame with multiple columns, null values in descending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(0, None, "Z"), (1, "Bob", None), (2, "Alice", "Y")], ["age", "name", "grade"])
>>> df.sort(sf.desc_nulls_last(df.name), sf.desc_nulls_last(df.grade)).show()
+---+-----+-----+
|age| name|grade|
+---+-----+-----+
| 1| Bob| NULL|
| 2|Alice| Y|
| 0| NULL| Z|
+---+-----+-----+
Example 3: Sorting a DataFrame with null values in descending order using column name string
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(0, None), (1, "Bob"), (2, "Alice")], ["age", "name"])
>>> df.sort(sf.desc_nulls_last("name")).show()
+---+-----+
|age| name|
+---+-----+
| 1| Bob|
| 2|Alice|
| 0| NULL|
+---+-----+
"""
return (
col.desc_nulls_last()
if isinstance(col, Column)
else _invoke_function("desc_nulls_last", col)
)
@_try_remote_functions
def stddev(col: "ColumnOrName") -> Column:
"""
Aggregate function: alias for stddev_samp.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
See Also
--------
:meth:`pyspark.sql.functions.std`
:meth:`pyspark.sql.functions.stddev_pop`
:meth:`pyspark.sql.functions.stddev_samp`
:meth:`pyspark.sql.functions.variance`
:meth:`pyspark.sql.functions.skewness`
:meth:`pyspark.sql.functions.kurtosis`
Returns
-------
:class:`~pyspark.sql.Column`
standard deviation of given column.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(6).select(sf.stddev("id")).show()
+------------------+
| stddev(id)|
+------------------+
|1.8708286933869...|
+------------------+
"""
return _invoke_function_over_columns("stddev", col)
@_try_remote_functions
def std(col: "ColumnOrName") -> Column:
"""
Aggregate function: alias for stddev_samp.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
standard deviation of given column.
See Also
--------
:meth:`pyspark.sql.functions.stddev`
:meth:`pyspark.sql.functions.stddev_pop`
:meth:`pyspark.sql.functions.stddev_samp`
:meth:`pyspark.sql.functions.variance`
:meth:`pyspark.sql.functions.skewness`
:meth:`pyspark.sql.functions.kurtosis`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(6).select(sf.std("id")).show()
+------------------+
| std(id)|
+------------------+
|1.8708286933869...|
+------------------+
"""
return _invoke_function_over_columns("std", col)
@_try_remote_functions
def stddev_samp(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the unbiased sample standard deviation of
the expression in a group.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
standard deviation of given column.
See Also
--------
:meth:`pyspark.sql.functions.std`
:meth:`pyspark.sql.functions.stddev`
:meth:`pyspark.sql.functions.stddev_pop`
:meth:`pyspark.sql.functions.var_samp`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(6).select(sf.stddev_samp("id")).show()
+------------------+
| stddev_samp(id)|
+------------------+
|1.8708286933869...|
+------------------+
"""
return _invoke_function_over_columns("stddev_samp", col)
@_try_remote_functions
def stddev_pop(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns population standard deviation of
the expression in a group.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
standard deviation of given column.
See Also
--------
:meth:`pyspark.sql.functions.std`
:meth:`pyspark.sql.functions.stddev`
:meth:`pyspark.sql.functions.stddev_samp`
:meth:`pyspark.sql.functions.var_pop`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(6).select(sf.stddev_pop("id")).show()
+-----------------+
| stddev_pop(id)|
+-----------------+
|1.707825127659...|
+-----------------+
"""
return _invoke_function_over_columns("stddev_pop", col)
@_try_remote_functions
def variance(col: "ColumnOrName") -> Column:
"""
Aggregate function: alias for var_samp
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
variance of given column.
See Also
--------
:meth:`pyspark.sql.functions.var_pop`
:meth:`pyspark.sql.functions.var_samp`
:meth:`pyspark.sql.functions.stddev`
:meth:`pyspark.sql.functions.skewness`
:meth:`pyspark.sql.functions.kurtosis`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.range(6)
>>> df.select(sf.variance(df.id)).show()
+------------+
|variance(id)|
+------------+
| 3.5|
+------------+
"""
return _invoke_function_over_columns("variance", col)
@_try_remote_functions
def var_samp(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the unbiased sample variance of
the values in a group.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
variance of given column.
See Also
--------
:meth:`pyspark.sql.functions.variance`
:meth:`pyspark.sql.functions.var_pop`
:meth:`pyspark.sql.functions.std_samp`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.range(6)
>>> df.select(sf.var_samp(df.id)).show()
+------------+
|var_samp(id)|
+------------+
| 3.5|
+------------+
"""
return _invoke_function_over_columns("var_samp", col)
@_try_remote_functions
def var_pop(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the population variance of the values in a group.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
variance of given column.
See Also
--------
:meth:`pyspark.sql.functions.variance`
:meth:`pyspark.sql.functions.var_samp`
:meth:`pyspark.sql.functions.std_pop`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.range(6)
>>> df.select(sf.var_pop(df.id)).show()
+------------------+
| var_pop(id)|
+------------------+
|2.9166666666666...|
+------------------+
"""
return _invoke_function_over_columns("var_pop", col)
@_try_remote_functions
def regr_avgx(y: "ColumnOrName", x: "ColumnOrName") -> Column:
"""
Aggregate function: returns the average of the independent variable for non-null pairs
in a group, where `y` is the dependent variable and `x` is the independent variable.
.. versionadded:: 3.5.0
Parameters
----------
y : :class:`~pyspark.sql.Column` or column name
the dependent variable.
x : :class:`~pyspark.sql.Column` or column name
the independent variable.
Returns
-------
:class:`~pyspark.sql.Column`
the average of the independent variable for non-null pairs in a group.
See Also
--------
:meth:`pyspark.sql.functions.regr_avgy`
:meth:`pyspark.sql.functions.regr_count`
:meth:`pyspark.sql.functions.regr_intercept`
:meth:`pyspark.sql.functions.regr_r2`
:meth:`pyspark.sql.functions.regr_slope`
:meth:`pyspark.sql.functions.regr_sxy`
:meth:`pyspark.sql.functions.regr_syy`
Examples
--------
Example 1: All pairs are non-null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 2), (2, 2), (2, 3), (2, 4) AS tab(y, x)")
>>> df.select(sf.regr_avgx("y", "x"), sf.avg("x")).show()
+---------------+------+
|regr_avgx(y, x)|avg(x)|
+---------------+------+
| 2.75| 2.75|
+---------------+------+
Example 2: All pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
>>> df.select(sf.regr_avgx("y", "x"), sf.avg("x")).show()
+---------------+------+
|regr_avgx(y, x)|avg(x)|
+---------------+------+
| NULL| NULL|
+---------------+------+
Example 3: All pairs' y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
>>> df.select(sf.regr_avgx("y", "x"), sf.avg("x")).show()
+---------------+------+
|regr_avgx(y, x)|avg(x)|
+---------------+------+
| NULL| 1.0|
+---------------+------+
Example 4: Some pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 2), (2, null), (2, 3), (2, 4) AS tab(y, x)")
>>> df.select(sf.regr_avgx("y", "x"), sf.avg("x")).show()
+---------------+------+
|regr_avgx(y, x)|avg(x)|
+---------------+------+
| 3.0| 3.0|
+---------------+------+
Example 5: Some pairs' x or y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 2), (2, null), (null, 3), (2, 4) AS tab(y, x)")
>>> df.select(sf.regr_avgx("y", "x"), sf.avg("x")).show()
+---------------+------+
|regr_avgx(y, x)|avg(x)|
+---------------+------+
| 3.0| 3.0|
+---------------+------+
"""
return _invoke_function_over_columns("regr_avgx", y, x)
@_try_remote_functions
def regr_avgy(y: "ColumnOrName", x: "ColumnOrName") -> Column:
"""
Aggregate function: returns the average of the dependent variable for non-null pairs
in a group, where `y` is the dependent variable and `x` is the independent variable.
.. versionadded:: 3.5.0
Parameters
----------
y : :class:`~pyspark.sql.Column` or column name
the dependent variable.
x : :class:`~pyspark.sql.Column` or column name
the independent variable.
Returns
-------
:class:`~pyspark.sql.Column`
the average of the dependent variable for non-null pairs in a group.
See Also
--------
:meth:`pyspark.sql.functions.regr_avgx`
:meth:`pyspark.sql.functions.regr_count`
:meth:`pyspark.sql.functions.regr_intercept`
:meth:`pyspark.sql.functions.regr_r2`
:meth:`pyspark.sql.functions.regr_slope`
:meth:`pyspark.sql.functions.regr_sxy`
:meth:`pyspark.sql.functions.regr_syy`
Examples
--------
Example 1: All pairs are non-null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 2), (2, 2), (2, 3), (2, 4) AS tab(y, x)")
>>> df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()
+---------------+------+
|regr_avgy(y, x)|avg(y)|
+---------------+------+
| 1.75| 1.75|
+---------------+------+
Example 2: All pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
>>> df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()
+---------------+------+
|regr_avgy(y, x)|avg(y)|
+---------------+------+
| NULL| 1.0|
+---------------+------+
Example 3: All pairs' y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
>>> df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()
+---------------+------+
|regr_avgy(y, x)|avg(y)|
+---------------+------+
| NULL| NULL|
+---------------+------+
Example 4: Some pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 2), (2, null), (2, 3), (2, 4) AS tab(y, x)")
>>> df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()
+------------------+------+
| regr_avgy(y, x)|avg(y)|
+------------------+------+
|1.6666666666666...| 1.75|
+------------------+------+
Example 5: Some pairs' x or y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 2), (2, null), (null, 3), (2, 4) AS tab(y, x)")
>>> df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()
+---------------+------------------+
|regr_avgy(y, x)| avg(y)|
+---------------+------------------+
| 1.5|1.6666666666666...|
+---------------+------------------+
"""
return _invoke_function_over_columns("regr_avgy", y, x)
@_try_remote_functions
def regr_count(y: "ColumnOrName", x: "ColumnOrName") -> Column:
"""
Aggregate function: returns the number of non-null number pairs
in a group, where `y` is the dependent variable and `x` is the independent variable.
.. versionadded:: 3.5.0
Parameters
----------
y : :class:`~pyspark.sql.Column` or column name
the dependent variable.
x : :class:`~pyspark.sql.Column` or column name
the independent variable.
See Also
--------
:meth:`pyspark.sql.functions.regr_avgx`
:meth:`pyspark.sql.functions.regr_avgy`
:meth:`pyspark.sql.functions.regr_intercept`
:meth:`pyspark.sql.functions.regr_r2`
:meth:`pyspark.sql.functions.regr_slope`
:meth:`pyspark.sql.functions.regr_sxy`
:meth:`pyspark.sql.functions.regr_syy`
Returns
-------
:class:`~pyspark.sql.Column`
the number of non-null number pairs in a group.
Examples
--------
Example 1: All pairs are non-null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 2), (2, 2), (2, 3), (2, 4) AS tab(y, x)")
>>> df.select(sf.regr_count("y", "x"), sf.count(sf.lit(0))).show()
+----------------+--------+
|regr_count(y, x)|count(0)|
+----------------+--------+
| 4| 4|
+----------------+--------+
Example 2: All pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
>>> df.select(sf.regr_count("y", "x"), sf.count(sf.lit(0))).show()
+----------------+--------+
|regr_count(y, x)|count(0)|
+----------------+--------+
| 0| 1|
+----------------+--------+
Example 3: All pairs' y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
>>> df.select(sf.regr_count("y", "x"), sf.count(sf.lit(0))).show()
+----------------+--------+
|regr_count(y, x)|count(0)|
+----------------+--------+
| 0| 1|
+----------------+--------+
Example 4: Some pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 2), (2, null), (2, 3), (2, 4) AS tab(y, x)")
>>> df.select(sf.regr_count("y", "x"), sf.count(sf.lit(0))).show()
+----------------+--------+
|regr_count(y, x)|count(0)|
+----------------+--------+
| 3| 4|
+----------------+--------+
Example 5: Some pairs' x or y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 2), (2, null), (null, 3), (2, 4) AS tab(y, x)")
>>> df.select(sf.regr_count("y", "x"), sf.count(sf.lit(0))).show()
+----------------+--------+
|regr_count(y, x)|count(0)|
+----------------+--------+
| 2| 4|
+----------------+--------+
"""
return _invoke_function_over_columns("regr_count", y, x)
@_try_remote_functions
def regr_intercept(y: "ColumnOrName", x: "ColumnOrName") -> Column:
"""
Aggregate function: returns the intercept of the univariate linear regression line
for non-null pairs in a group, where `y` is the dependent variable and
`x` is the independent variable.
.. versionadded:: 3.5.0
Parameters
----------
y : :class:`~pyspark.sql.Column` or column name
the dependent variable.
x : :class:`~pyspark.sql.Column` or column name
the independent variable.
Returns
-------
:class:`~pyspark.sql.Column`
the intercept of the univariate linear regression line for non-null pairs in a group.
See Also
--------
:meth:`pyspark.sql.functions.regr_avgx`
:meth:`pyspark.sql.functions.regr_avgy`
:meth:`pyspark.sql.functions.regr_count`
:meth:`pyspark.sql.functions.regr_r2`
:meth:`pyspark.sql.functions.regr_slope`
:meth:`pyspark.sql.functions.regr_sxy`
:meth:`pyspark.sql.functions.regr_syy`
Examples
--------
Example 1: All pairs are non-null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, 2), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_intercept("y", "x")).show()
+--------------------+
|regr_intercept(y, x)|
+--------------------+
| 0.0|
+--------------------+
Example 2: All pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
>>> df.select(sf.regr_intercept("y", "x")).show()
+--------------------+
|regr_intercept(y, x)|
+--------------------+
| NULL|
+--------------------+
Example 3: All pairs' y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
>>> df.select(sf.regr_intercept("y", "x")).show()
+--------------------+
|regr_intercept(y, x)|
+--------------------+
| NULL|
+--------------------+
Example 4: Some pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_intercept("y", "x")).show()
+--------------------+
|regr_intercept(y, x)|
+--------------------+
| 0.0|
+--------------------+
Example 5: Some pairs' x or y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (null, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_intercept("y", "x")).show()
+--------------------+
|regr_intercept(y, x)|
+--------------------+
| 0.0|
+--------------------+
"""
return _invoke_function_over_columns("regr_intercept", y, x)
@_try_remote_functions
def regr_r2(y: "ColumnOrName", x: "ColumnOrName") -> Column:
"""
Aggregate function: returns the coefficient of determination for non-null pairs
in a group, where `y` is the dependent variable and `x` is the independent variable.
.. versionadded:: 3.5.0
Parameters
----------
y : :class:`~pyspark.sql.Column` or column name
the dependent variable.
x : :class:`~pyspark.sql.Column` or column name
the independent variable.
Returns
-------
:class:`~pyspark.sql.Column`
the coefficient of determination for non-null pairs in a group.
See Also
--------
:meth:`pyspark.sql.functions.regr_avgx`
:meth:`pyspark.sql.functions.regr_avgy`
:meth:`pyspark.sql.functions.regr_count`
:meth:`pyspark.sql.functions.regr_intercept`
:meth:`pyspark.sql.functions.regr_slope`
:meth:`pyspark.sql.functions.regr_sxy`
:meth:`pyspark.sql.functions.regr_syy`
Examples
--------
Example 1: All pairs are non-null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, 2), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_r2("y", "x")).show()
+-------------+
|regr_r2(y, x)|
+-------------+
| 1.0|
+-------------+
Example 2: All pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
>>> df.select(sf.regr_r2("y", "x")).show()
+-------------+
|regr_r2(y, x)|
+-------------+
| NULL|
+-------------+
Example 3: All pairs' y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
>>> df.select(sf.regr_r2("y", "x")).show()
+-------------+
|regr_r2(y, x)|
+-------------+
| NULL|
+-------------+
Example 4: Some pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_r2("y", "x")).show()
+-------------+
|regr_r2(y, x)|
+-------------+
| 1.0|
+-------------+
Example 5: Some pairs' x or y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (null, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_r2("y", "x")).show()
+-------------+
|regr_r2(y, x)|
+-------------+
| 1.0|
+-------------+
"""
return _invoke_function_over_columns("regr_r2", y, x)
@_try_remote_functions
def regr_slope(y: "ColumnOrName", x: "ColumnOrName") -> Column:
"""
Aggregate function: returns the slope of the linear regression line for non-null pairs
in a group, where `y` is the dependent variable and `x` is the independent variable.
.. versionadded:: 3.5.0
Parameters
----------
y : :class:`~pyspark.sql.Column` or column name
the dependent variable.
x : :class:`~pyspark.sql.Column` or column name
the independent variable.
Returns
-------
:class:`~pyspark.sql.Column`
the slope of the linear regression line for non-null pairs in a group.
See Also
--------
:meth:`pyspark.sql.functions.regr_avgx`
:meth:`pyspark.sql.functions.regr_avgy`
:meth:`pyspark.sql.functions.regr_count`
:meth:`pyspark.sql.functions.regr_intercept`
:meth:`pyspark.sql.functions.regr_r2`
:meth:`pyspark.sql.functions.regr_sxy`
:meth:`pyspark.sql.functions.regr_syy`
Examples
--------
Example 1: All pairs are non-null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, 2), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_slope("y", "x")).show()
+----------------+
|regr_slope(y, x)|
+----------------+
| 1.0|
+----------------+
Example 2: All pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
>>> df.select(sf.regr_slope("y", "x")).show()
+----------------+
|regr_slope(y, x)|
+----------------+
| NULL|
+----------------+
Example 3: All pairs' y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
>>> df.select(sf.regr_slope("y", "x")).show()
+----------------+
|regr_slope(y, x)|
+----------------+
| NULL|
+----------------+
Example 4: Some pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_slope("y", "x")).show()
+----------------+
|regr_slope(y, x)|
+----------------+
| 1.0|
+----------------+
Example 5: Some pairs' x or y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (null, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_slope("y", "x")).show()
+----------------+
|regr_slope(y, x)|
+----------------+
| 1.0|
+----------------+
"""
return _invoke_function_over_columns("regr_slope", y, x)
@_try_remote_functions
def regr_sxx(y: "ColumnOrName", x: "ColumnOrName") -> Column:
"""
Aggregate function: returns REGR_COUNT(y, x) * VAR_POP(x) for non-null pairs
in a group, where `y` is the dependent variable and `x` is the independent variable.
.. versionadded:: 3.5.0
Parameters
----------
y : :class:`~pyspark.sql.Column` or column name
the dependent variable.
x : :class:`~pyspark.sql.Column` or column name
the independent variable.
Returns
-------
:class:`~pyspark.sql.Column`
REGR_COUNT(y, x) * VAR_POP(x) for non-null pairs in a group.
See Also
--------
:meth:`pyspark.sql.functions.regr_avgx`
:meth:`pyspark.sql.functions.regr_avgy`
:meth:`pyspark.sql.functions.regr_count`
:meth:`pyspark.sql.functions.regr_intercept`
:meth:`pyspark.sql.functions.regr_r2`
:meth:`pyspark.sql.functions.regr_sxy`
:meth:`pyspark.sql.functions.regr_syy`
Examples
--------
Example 1: All pairs are non-null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, 2), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_sxx("y", "x")).show()
+--------------+
|regr_sxx(y, x)|
+--------------+
| 5.0|
+--------------+
Example 2: All pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
>>> df.select(sf.regr_sxx("y", "x")).show()
+--------------+
|regr_sxx(y, x)|
+--------------+
| NULL|
+--------------+
Example 3: All pairs' y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
>>> df.select(sf.regr_sxx("y", "x")).show()
+--------------+
|regr_sxx(y, x)|
+--------------+
| NULL|
+--------------+
Example 4: Some pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_sxx("y", "x")).show()
+-----------------+
| regr_sxx(y, x)|
+-----------------+
|4.666666666666...|
+-----------------+
Example 5: Some pairs' x or y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (null, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_sxx("y", "x")).show()
+--------------+
|regr_sxx(y, x)|
+--------------+
| 4.5|
+--------------+
"""
return _invoke_function_over_columns("regr_sxx", y, x)
@_try_remote_functions
def regr_sxy(y: "ColumnOrName", x: "ColumnOrName") -> Column:
"""
Aggregate function: returns REGR_COUNT(y, x) * COVAR_POP(y, x) for non-null pairs
in a group, where `y` is the dependent variable and `x` is the independent variable.
.. versionadded:: 3.5.0
Parameters
----------
y : :class:`~pyspark.sql.Column` or column name
the dependent variable.
x : :class:`~pyspark.sql.Column` or column name
the independent variable.
See Also
--------
:meth:`pyspark.sql.functions.regr_avgx`
:meth:`pyspark.sql.functions.regr_avgy`
:meth:`pyspark.sql.functions.regr_count`
:meth:`pyspark.sql.functions.regr_intercept`
:meth:`pyspark.sql.functions.regr_r2`
:meth:`pyspark.sql.functions.regr_slope`
:meth:`pyspark.sql.functions.regr_syy`
Returns
-------
:class:`~pyspark.sql.Column`
REGR_COUNT(y, x) * COVAR_POP(y, x) for non-null pairs in a group.
Examples
--------
Example 1: All pairs are non-null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, 2), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_sxy("y", "x")).show()
+--------------+
|regr_sxy(y, x)|
+--------------+
| 5.0|
+--------------+
Example 2: All pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
>>> df.select(sf.regr_sxy("y", "x")).show()
+--------------+
|regr_sxy(y, x)|
+--------------+
| NULL|
+--------------+
Example 3: All pairs' y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
>>> df.select(sf.regr_sxy("y", "x")).show()
+--------------+
|regr_sxy(y, x)|
+--------------+
| NULL|
+--------------+
Example 4: Some pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_sxy("y", "x")).show()
+-----------------+
| regr_sxy(y, x)|
+-----------------+
|4.666666666666...|
+-----------------+
Example 5: Some pairs' x or y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (null, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_sxy("y", "x")).show()
+--------------+
|regr_sxy(y, x)|
+--------------+
| 4.5|
+--------------+
"""
return _invoke_function_over_columns("regr_sxy", y, x)
@_try_remote_functions
def regr_syy(y: "ColumnOrName", x: "ColumnOrName") -> Column:
"""
Aggregate function: returns REGR_COUNT(y, x) * VAR_POP(y) for non-null pairs
in a group, where `y` is the dependent variable and `x` is the independent variable.
.. versionadded:: 3.5.0
Parameters
----------
y : :class:`~pyspark.sql.Column` or column name
the dependent variable.
x : :class:`~pyspark.sql.Column` or column name
the independent variable.
Returns
-------
:class:`~pyspark.sql.Column`
REGR_COUNT(y, x) * VAR_POP(y) for non-null pairs in a group.
See Also
--------
:meth:`pyspark.sql.functions.regr_avgx`
:meth:`pyspark.sql.functions.regr_avgy`
:meth:`pyspark.sql.functions.regr_count`
:meth:`pyspark.sql.functions.regr_intercept`
:meth:`pyspark.sql.functions.regr_r2`
:meth:`pyspark.sql.functions.regr_slope`
:meth:`pyspark.sql.functions.regr_sxy`
Examples
--------
Example 1: All pairs are non-null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, 2), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_syy("y", "x")).show()
+--------------+
|regr_syy(y, x)|
+--------------+
| 5.0|
+--------------+
Example 2: All pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
>>> df.select(sf.regr_syy("y", "x")).show()
+--------------+
|regr_syy(y, x)|
+--------------+
| NULL|
+--------------+
Example 3: All pairs' y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
>>> df.select(sf.regr_syy("y", "x")).show()
+--------------+
|regr_syy(y, x)|
+--------------+
| NULL|
+--------------+
Example 4: Some pairs' x values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_syy("y", "x")).show()
+-----------------+
| regr_syy(y, x)|
+-----------------+
|4.666666666666...|
+-----------------+
Example 5: Some pairs' x or y values are null
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (null, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_syy("y", "x")).show()
+--------------+
|regr_syy(y, x)|
+--------------+
| 4.5|
+--------------+
"""
return _invoke_function_over_columns("regr_syy", y, x)
@_try_remote_functions
def every(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns true if all values of `col` are true.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to check if all values are true.
See Also
--------
:meth:`pyspark.sql.functions.some`
Returns
-------
:class:`~pyspark.sql.Column`
true if all values of `col` are true, false otherwise.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [[True], [True], [True]], ["flag"]
... ).select(sf.every("flag")).show()
+-----------+
|every(flag)|
+-----------+
| true|
+-----------+
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [[True], [False], [True]], ["flag"]
... ).select(sf.every("flag")).show()
+-----------+
|every(flag)|
+-----------+
| false|
+-----------+
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [[False], [False], [False]], ["flag"]
... ).select(sf.every("flag")).show()
+-----------+
|every(flag)|
+-----------+
| false|
+-----------+
"""
return _invoke_function_over_columns("every", col)
@_try_remote_functions
def bool_and(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns true if all values of `col` are true.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to check if all values are true.
Returns
-------
:class:`~pyspark.sql.Column`
true if all values of `col` are true, false otherwise.
See Also
--------
:meth:`pyspark.sql.functions.bool_or`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[True], [True], [True]], ["flag"])
>>> df.select(sf.bool_and("flag")).show()
+--------------+
|bool_and(flag)|
+--------------+
| true|
+--------------+
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[True], [False], [True]], ["flag"])
>>> df.select(sf.bool_and("flag")).show()
+--------------+
|bool_and(flag)|
+--------------+
| false|
+--------------+
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[False], [False], [False]], ["flag"])
>>> df.select(sf.bool_and("flag")).show()
+--------------+
|bool_and(flag)|
+--------------+
| false|
+--------------+
"""
return _invoke_function_over_columns("bool_and", col)
@_try_remote_functions
def some(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns true if at least one value of `col` is true.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to check if at least one value is true.
Returns
-------
:class:`~pyspark.sql.Column`
true if at least one value of `col` is true, false otherwise.
See Also
--------
:meth:`pyspark.sql.functions.every`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [[True], [True], [True]], ["flag"]
... ).select(sf.some("flag")).show()
+----------+
|some(flag)|
+----------+
| true|
+----------+
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [[True], [False], [True]], ["flag"]
... ).select(sf.some("flag")).show()
+----------+
|some(flag)|
+----------+
| true|
+----------+
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [[False], [False], [False]], ["flag"]
... ).select(sf.some("flag")).show()
+----------+
|some(flag)|
+----------+
| false|
+----------+
"""
return _invoke_function_over_columns("some", col)
@_try_remote_functions
def bool_or(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns true if at least one value of `col` is true.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to check if at least one value is true.
Returns
-------
:class:`~pyspark.sql.Column`
true if at least one value of `col` is true, false otherwise.
See Also
--------
:meth:`pyspark.sql.functions.bool_and`
Examples
--------
>>> df = spark.createDataFrame([[True], [True], [True]], ["flag"])
>>> df.select(bool_or("flag")).show()
+-------------+
|bool_or(flag)|
+-------------+
| true|
+-------------+
>>> df = spark.createDataFrame([[True], [False], [True]], ["flag"])
>>> df.select(bool_or("flag")).show()
+-------------+
|bool_or(flag)|
+-------------+
| true|
+-------------+
>>> df = spark.createDataFrame([[False], [False], [False]], ["flag"])
>>> df.select(bool_or("flag")).show()
+-------------+
|bool_or(flag)|
+-------------+
| false|
+-------------+
"""
return _invoke_function_over_columns("bool_or", col)
@_try_remote_functions
def bit_and(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the bitwise AND of all non-null input values, or null if none.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the bitwise AND of all non-null input values, or null if none.
See Also
--------
:meth:`pyspark.sql.functions.bit_or`
:meth:`pyspark.sql.functions.bit_xor`
Examples
--------
Example 1: Bitwise AND with all non-null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[1],[2]], ["c"])
>>> df.select(sf.bit_and("c")).show()
+----------+
|bit_and(c)|
+----------+
| 0|
+----------+
Example 2: Bitwise AND with null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[None],[2]], ["c"])
>>> df.select(sf.bit_and("c")).show()
+----------+
|bit_and(c)|
+----------+
| 0|
+----------+
Example 3: Bitwise AND with all null values
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import IntegerType, StructType, StructField
>>> schema = StructType([StructField("c", IntegerType(), True)])
>>> df = spark.createDataFrame([[None],[None],[None]], schema=schema)
>>> df.select(sf.bit_and("c")).show()
+----------+
|bit_and(c)|
+----------+
| NULL|
+----------+
Example 4: Bitwise AND with single input value
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[5]], ["c"])
>>> df.select(sf.bit_and("c")).show()
+----------+
|bit_and(c)|
+----------+
| 5|
+----------+
"""
return _invoke_function_over_columns("bit_and", col)
@_try_remote_functions
def bit_or(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the bitwise OR of all non-null input values, or null if none.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the bitwise OR of all non-null input values, or null if none.
See Also
--------
:meth:`pyspark.sql.functions.bit_and`
:meth:`pyspark.sql.functions.bit_xor`
Examples
--------
Example 1: Bitwise OR with all non-null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[1],[2]], ["c"])
>>> df.select(sf.bit_or("c")).show()
+---------+
|bit_or(c)|
+---------+
| 3|
+---------+
Example 2: Bitwise OR with some null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[None],[2]], ["c"])
>>> df.select(sf.bit_or("c")).show()
+---------+
|bit_or(c)|
+---------+
| 3|
+---------+
Example 3: Bitwise OR with all null values
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import IntegerType, StructType, StructField
>>> schema = StructType([StructField("c", IntegerType(), True)])
>>> df = spark.createDataFrame([[None],[None],[None]], schema=schema)
>>> df.select(sf.bit_or("c")).show()
+---------+
|bit_or(c)|
+---------+
| NULL|
+---------+
Example 4: Bitwise OR with single input value
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[5]], ["c"])
>>> df.select(sf.bit_or("c")).show()
+---------+
|bit_or(c)|
+---------+
| 5|
+---------+
"""
return _invoke_function_over_columns("bit_or", col)
@_try_remote_functions
def bit_xor(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the bitwise XOR of all non-null input values, or null if none.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the bitwise XOR of all non-null input values, or null if none.
See Also
--------
:meth:`pyspark.sql.functions.bit_and`
:meth:`pyspark.sql.functions.bit_or`
Examples
--------
Example 1: Bitwise XOR with all non-null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[1],[2]], ["c"])
>>> df.select(sf.bit_xor("c")).show()
+----------+
|bit_xor(c)|
+----------+
| 2|
+----------+
Example 2: Bitwise XOR with some null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[None],[2]], ["c"])
>>> df.select(sf.bit_xor("c")).show()
+----------+
|bit_xor(c)|
+----------+
| 3|
+----------+
Example 3: Bitwise XOR with all null values
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import IntegerType, StructType, StructField
>>> schema = StructType([StructField("c", IntegerType(), True)])
>>> df = spark.createDataFrame([[None],[None],[None]], schema=schema)
>>> df.select(sf.bit_xor("c")).show()
+----------+
|bit_xor(c)|
+----------+
| NULL|
+----------+
Example 4: Bitwise XOR with single input value
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[5]], ["c"])
>>> df.select(sf.bit_xor("c")).show()
+----------+
|bit_xor(c)|
+----------+
| 5|
+----------+
"""
return _invoke_function_over_columns("bit_xor", col)
@_try_remote_functions
def skewness(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the skewness of the values in a group.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
See Also
--------
:meth:`pyspark.sql.functions.std`
:meth:`pyspark.sql.functions.stddev`
:meth:`pyspark.sql.functions.variance`
:meth:`pyspark.sql.functions.kurtosis`
Returns
-------
:class:`~pyspark.sql.Column`
skewness of given column.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[1],[2]], ["c"])
>>> df.select(sf.skewness(df.c)).show()
+------------------+
| skewness(c)|
+------------------+
|0.7071067811865...|
+------------------+
"""
return _invoke_function_over_columns("skewness", col)
@_try_remote_functions
def kurtosis(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the kurtosis of the values in a group.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
kurtosis of given column.
See Also
--------
:meth:`pyspark.sql.functions.std`
:meth:`pyspark.sql.functions.stddev`
:meth:`pyspark.sql.functions.variance`
:meth:`pyspark.sql.functions.skewness`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[1],[2]], ["c"])
>>> df.select(sf.kurtosis(df.c)).show()
+-----------+
|kurtosis(c)|
+-----------+
| -1.5|
+-----------+
"""
return _invoke_function_over_columns("kurtosis", col)
@_try_remote_functions
def collect_list(col: "ColumnOrName") -> Column:
"""
Aggregate function: Collects the values from a column into a list,
maintaining duplicates, and returns this list of objects.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column on which the function is computed.
See Also
--------
:meth:`pyspark.sql.functions.array_agg`
:meth:`pyspark.sql.functions.collect_set`
Returns
-------
:class:`~pyspark.sql.Column`
A new Column object representing a list of collected values, with duplicate values included.
Notes
-----
The function is non-deterministic as the order of collected results depends
on the order of the rows, which possibly becomes non-deterministic after shuffle operations.
Examples
--------
Example 1: Collect values from a DataFrame and sort the result in ascending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,), (2,), (2,)], ('value',))
>>> df.select(sf.sort_array(sf.collect_list('value')).alias('sorted_list')).show()
+-----------+
|sorted_list|
+-----------+
| [1, 2, 2]|
+-----------+
Example 2: Collect values from a DataFrame and sort the result in descending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(2,), (5,), (5,)], ('age',))
>>> df.select(sf.sort_array(sf.collect_list('age'), asc=False).alias('sorted_list')).show()
+-----------+
|sorted_list|
+-----------+
| [5, 5, 2]|
+-----------+
Example 3: Collect values from a DataFrame with multiple columns and sort the result
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, "John"), (2, "John"), (3, "Ana")], ("id", "name"))
>>> df = df.groupBy("name").agg(sf.sort_array(sf.collect_list('id')).alias('sorted_list'))
>>> df.orderBy(sf.desc("name")).show()
+----+-----------+
|name|sorted_list|
+----+-----------+
|John| [1, 2]|
| Ana| [3]|
+----+-----------+
"""
return _invoke_function_over_columns("collect_list", col)
@_try_remote_functions
def array_agg(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns a list of objects with duplicates.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
list of objects with duplicates.
See Also
--------
:meth:`pyspark.sql.functions.collect_list`
:meth:`pyspark.sql.functions.collect_set`
Examples
--------
Example 1: Using array_agg function on an int column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[1],[2]], ["c"])
>>> df.agg(sf.sort_array(sf.array_agg('c')).alias('sorted_list')).show()
+-----------+
|sorted_list|
+-----------+
| [1, 1, 2]|
+-----------+
Example 2: Using array_agg function on a string column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([["apple"],["apple"],["banana"]], ["c"])
>>> df.agg(sf.sort_array(sf.array_agg('c')).alias('sorted_list')).show(truncate=False)
+----------------------+
|sorted_list |
+----------------------+
|[apple, apple, banana]|
+----------------------+
Example 3: Using array_agg function on a column with null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],[None],[2]], ["c"])
>>> df.agg(sf.sort_array(sf.array_agg('c')).alias('sorted_list')).show()
+-----------+
|sorted_list|
+-----------+
| [1, 2]|
+-----------+
Example 4: Using array_agg function on a column with different data types
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([[1],["apple"],[2]], ["c"])
>>> df.agg(sf.sort_array(sf.array_agg('c')).alias('sorted_list')).show()
+-------------+
| sorted_list|
+-------------+
|[1, 2, apple]|
+-------------+
"""
return _invoke_function_over_columns("array_agg", col)
@_try_remote_functions
def collect_set(col: "ColumnOrName") -> Column:
"""
Aggregate function: Collects the values from a column into a set,
eliminating duplicates, and returns this set of objects.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column on which the function is computed.
Returns
-------
:class:`~pyspark.sql.Column`
A new Column object representing a set of collected values, duplicates excluded.
See Also
--------
:meth:`pyspark.sql.functions.array_agg`
:meth:`pyspark.sql.functions.collect_list`
Notes
-----
This function is non-deterministic as the order of collected results depends
on the order of the rows, which may be non-deterministic after any shuffle operations.
Examples
--------
Example 1: Collect values from a DataFrame and sort the result in ascending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,), (2,), (2,)], ('value',))
>>> df.select(sf.sort_array(sf.collect_set('value')).alias('sorted_set')).show()
+----------+
|sorted_set|
+----------+
| [1, 2]|
+----------+
Example 2: Collect values from a DataFrame and sort the result in descending order
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(2,), (5,), (5,)], ('age',))
>>> df.select(sf.sort_array(sf.collect_set('age'), asc=False).alias('sorted_set')).show()
+----------+
|sorted_set|
+----------+
| [5, 2]|
+----------+
Example 3: Collect values from a DataFrame with multiple columns and sort the result
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, "John"), (2, "John"), (3, "Ana")], ("id", "name"))
>>> df = df.groupBy("name").agg(sf.sort_array(sf.collect_set('id')).alias('sorted_set'))
>>> df.orderBy(sf.desc("name")).show()
+----+----------+
|name|sorted_set|
+----+----------+
|John| [1, 2]|
| Ana| [3]|
+----+----------+
"""
return _invoke_function_over_columns("collect_set", col)
@_try_remote_functions
def degrees(col: "ColumnOrName") -> Column:
"""
Converts an angle measured in radians to an approximately equivalent angle
measured in degrees.
.. versionadded:: 2.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
angle in radians
Returns
-------
:class:`~pyspark.sql.Column`
angle in degrees, as if computed by `java.lang.Math.toDegrees()`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (0.0), (PI()), (PI() / 2), (PI() / 4) AS TAB(value)"
... ).select("*", sf.degrees("value")).show()
+------------------+--------------+
| value|DEGREES(value)|
+------------------+--------------+
| 0.0| 0.0|
| 3.141592653589...| 180.0|
|1.5707963267948...| 90.0|
|0.7853981633974...| 45.0|
+------------------+--------------+
"""
return _invoke_function_over_columns("degrees", col)
@_try_remote_functions
def radians(col: "ColumnOrName") -> Column:
"""
Converts an angle measured in degrees to an approximately equivalent angle
measured in radians.
.. versionadded:: 2.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
angle in degrees
Returns
-------
:class:`~pyspark.sql.Column`
angle in radians, as if computed by `java.lang.Math.toRadians()`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.sql(
... "SELECT * FROM VALUES (180), (90), (45), (0) AS TAB(value)"
... ).select("*", sf.radians("value")).show()
+-----+------------------+
|value| RADIANS(value)|
+-----+------------------+
| 180| 3.141592653589...|
| 90|1.5707963267948...|
| 45|0.7853981633974...|
| 0| 0.0|
+-----+------------------+
"""
return _invoke_function_over_columns("radians", col)
@_try_remote_functions
def atan2(col1: Union["ColumnOrName", float], col2: Union["ColumnOrName", float]) -> Column:
"""
Compute the angle in radians between the positive x-axis of a plane
and the point given by the coordinates
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column`, column name or float
coordinate on y-axis
col2 : :class:`~pyspark.sql.Column`, column name or float
coordinate on x-axis
Returns
-------
:class:`~pyspark.sql.Column`
the `theta` component of the point
(`r`, `theta`)
in polar coordinates that corresponds to the point
(`x`, `y`) in Cartesian coordinates,
as if computed by `java.lang.Math.atan2()`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.atan2(sf.lit(1), sf.lit(2))).show()
+------------------+
| ATAN2(1, 2)|
+------------------+
|0.4636476090008...|
+------------------+
"""
return _invoke_binary_math_function("atan2", col1, col2)
@_try_remote_functions
def hypot(col1: Union["ColumnOrName", float], col2: Union["ColumnOrName", float]) -> Column:
"""
Computes ``sqrt(a^2 + b^2)`` without intermediate overflow or underflow.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column`, column name or float
a leg.
col2 : :class:`~pyspark.sql.Column`, column name or float
b leg.
Returns
-------
:class:`~pyspark.sql.Column`
length of the hypotenuse.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.hypot(sf.lit(1), sf.lit(2))).show()
+----------------+
| HYPOT(1, 2)|
+----------------+
|2.23606797749...|
+----------------+
"""
return _invoke_binary_math_function("hypot", col1, col2)
@_try_remote_functions
def pow(col1: Union["ColumnOrName", float], col2: Union["ColumnOrName", float]) -> Column:
"""
Returns the value of the first argument raised to the power of the second argument.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column`, column name or float
the base number.
col2 : :class:`~pyspark.sql.Column`, column name or float
the exponent number.
Returns
-------
:class:`~pyspark.sql.Column`
the base rased to the power the argument.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(5).select("*", sf.pow("id", 2)).show()
+---+------------+
| id|POWER(id, 2)|
+---+------------+
| 0| 0.0|
| 1| 1.0|
| 2| 4.0|
| 3| 9.0|
| 4| 16.0|
+---+------------+
"""
return _invoke_binary_math_function("pow", col1, col2)
power = pow
@_try_remote_functions
def pmod(dividend: Union["ColumnOrName", float], divisor: Union["ColumnOrName", float]) -> Column:
"""
Returns the positive value of dividend mod divisor.
.. versionadded:: 3.4.0
Parameters
----------
dividend : :class:`~pyspark.sql.Column`, column name or float
the column that contains dividend, or the specified dividend value
divisor : :class:`~pyspark.sql.Column`, column name or float
the column that contains divisor, or the specified divisor value
Returns
-------
:class:`~pyspark.sql.Column`
positive value of dividend mod divisor.
Notes
-----
Supports Spark Connect.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (1.0, float('nan')), (float('nan'), 2.0), (10.0, 3.0),
... (float('nan'), float('nan')), (-3.0, 4.0), (-10.0, 3.0),
... (-5.0, -6.0), (7.0, -8.0), (1.0, 2.0)],
... ("a", "b"))
>>> df.select("*", sf.pmod("a", "b")).show()
+-----+----+----------+
| a| b|pmod(a, b)|
+-----+----+----------+
| 1.0| NaN| NaN|
| NaN| 2.0| NaN|
| 10.0| 3.0| 1.0|
| NaN| NaN| NaN|
| -3.0| 4.0| 1.0|
|-10.0| 3.0| 2.0|
| -5.0|-6.0| -5.0|
| 7.0|-8.0| 7.0|
| 1.0| 2.0| 1.0|
+-----+----+----------+
"""
return _invoke_binary_math_function("pmod", dividend, divisor)
@_try_remote_functions
def width_bucket(
v: "ColumnOrName",
min: "ColumnOrName",
max: "ColumnOrName",
numBucket: Union["ColumnOrName", int],
) -> Column:
"""
Returns the bucket number into which the value of this expression would fall
after being evaluated. Note that input arguments must follow conditions listed below;
otherwise, the method will return null.
.. versionadded:: 3.5.0
Parameters
----------
v : :class:`~pyspark.sql.Column` or column name
value to compute a bucket number in the histogram
min : :class:`~pyspark.sql.Column` or column name
minimum value of the histogram
max : :class:`~pyspark.sql.Column` or column name
maximum value of the histogram
numBucket : :class:`~pyspark.sql.Column`, column name or int
the number of buckets
Returns
-------
:class:`~pyspark.sql.Column`
the bucket number into which the value would fall after being evaluated
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (5.3, 0.2, 10.6, 5),
... (-2.1, 1.3, 3.4, 3),
... (8.1, 0.0, 5.7, 4),
... (-0.9, 5.2, 0.5, 2)],
... ['v', 'min', 'max', 'n'])
>>> df.select("*", sf.width_bucket('v', 'min', 'max', 'n')).show()
+----+---+----+---+----------------------------+
| v|min| max| n|width_bucket(v, min, max, n)|
+----+---+----+---+----------------------------+
| 5.3|0.2|10.6| 5| 3|
|-2.1|1.3| 3.4| 3| 0|
| 8.1|0.0| 5.7| 4| 5|
|-0.9|5.2| 0.5| 2| 3|
+----+---+----+---+----------------------------+
"""
numBucket = _enum_to_value(numBucket)
numBucket = lit(numBucket) if isinstance(numBucket, int) else numBucket
return _invoke_function_over_columns("width_bucket", v, min, max, numBucket)
@_try_remote_functions
def row_number() -> Column:
"""
Window function: returns a sequential number starting at 1 within a window partition.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`~pyspark.sql.Column`
the column for calculating row numbers.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql import Window
>>> df = spark.range(3)
>>> w = Window.orderBy(df.id.desc())
>>> df.withColumn("desc_order", sf.row_number().over(w)).show()
+---+----------+
| id|desc_order|
+---+----------+
| 2| 1|
| 1| 2|
| 0| 3|
+---+----------+
"""
return _invoke_function("row_number")
@_try_remote_functions
def dense_rank() -> Column:
"""
Window function: returns the rank of rows within a window partition, without any gaps.
The difference between rank and dense_rank is that dense_rank leaves no gaps in ranking
sequence when there are ties. That is, if you were ranking a competition using dense_rank
and had three people tie for second place, you would say that all three were in second
place and that the next person came in third. Rank would give me sequential numbers, making
the person that came in third place (after the ties) would register as coming in fifth.
This is equivalent to the DENSE_RANK function in SQL.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`~pyspark.sql.Column`
the column for calculating ranks.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame([1, 1, 2, 3, 3, 4], "int")
>>> w = Window.orderBy("value")
>>> df.withColumn("drank", sf.dense_rank().over(w)).show()
+-----+-----+
|value|drank|
+-----+-----+
| 1| 1|
| 1| 1|
| 2| 2|
| 3| 3|
| 3| 3|
| 4| 4|
+-----+-----+
"""
return _invoke_function("dense_rank")
@_try_remote_functions
def rank() -> Column:
"""
Window function: returns the rank of rows within a window partition.
The difference between rank and dense_rank is that dense_rank leaves no gaps in ranking
sequence when there are ties. That is, if you were ranking a competition using dense_rank
and had three people tie for second place, you would say that all three were in second
place and that the next person came in third. Rank would give me sequential numbers, making
the person that came in third place (after the ties) would register as coming in fifth.
This is equivalent to the RANK function in SQL.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`~pyspark.sql.Column`
the column for calculating ranks.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame([1, 1, 2, 3, 3, 4], "int")
>>> w = Window.orderBy("value")
>>> df.withColumn("drank", sf.rank().over(w)).show()
+-----+-----+
|value|drank|
+-----+-----+
| 1| 1|
| 1| 1|
| 2| 3|
| 3| 4|
| 3| 4|
| 4| 6|
+-----+-----+
"""
return _invoke_function("rank")
@_try_remote_functions
def cume_dist() -> Column:
"""
Window function: returns the cumulative distribution of values within a window partition,
i.e. the fraction of rows that are below the current row.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`~pyspark.sql.Column`
the column for calculating cumulative distribution.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame([1, 2, 3, 3, 4], "int")
>>> w = Window.orderBy("value")
>>> df.withColumn("cd", sf.cume_dist().over(w)).show()
+-----+---+
|value| cd|
+-----+---+
| 1|0.2|
| 2|0.4|
| 3|0.8|
| 3|0.8|
| 4|1.0|
+-----+---+
"""
return _invoke_function("cume_dist")
@_try_remote_functions
def percent_rank() -> Column:
"""
Window function: returns the relative rank (i.e. percentile) of rows within a window partition.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`~pyspark.sql.Column`
the column for calculating relative rank.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame([1, 1, 2, 3, 3, 4], "int")
>>> w = Window.orderBy("value")
>>> df.withColumn("pr", sf.percent_rank().over(w)).show()
+-----+---+
|value| pr|
+-----+---+
| 1|0.0|
| 1|0.0|
| 2|0.4|
| 3|0.6|
| 3|0.6|
| 4|1.0|
+-----+---+
"""
return _invoke_function("percent_rank")
@_try_remote_functions
def approxCountDistinct(col: "ColumnOrName", rsd: Optional[float] = None) -> Column:
"""
This aggregate function returns a new :class:`~pyspark.sql.Column`, which estimates
the approximate distinct count of elements in a specified column or a group of columns.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 2.1.0
Use :func:`approx_count_distinct` instead.
"""
warnings.warn("Deprecated in 2.1, use approx_count_distinct instead.", FutureWarning)
return approx_count_distinct(col, rsd)
@_try_remote_functions
def approx_count_distinct(col: "ColumnOrName", rsd: Optional[float] = None) -> Column:
"""
This aggregate function returns a new :class:`~pyspark.sql.Column`, which estimates
the approximate distinct count of elements in a specified column or a group of columns.
.. versionadded:: 2.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The label of the column to count distinct values in.
rsd : float, optional
The maximum allowed relative standard deviation (default = 0.05).
If rsd < 0.01, it would be more efficient to use :func:`count_distinct`.
Returns
-------
:class:`~pyspark.sql.Column`
A new Column object representing the approximate unique count.
See Also
--------
:meth:`pyspark.sql.functions.count_distinct`
Examples
--------
Example 1: Counting distinct values in a single column DataFrame representing integers
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([1,2,2,3], "int")
>>> df.agg(sf.approx_count_distinct("value")).show()
+----------------------------+
|approx_count_distinct(value)|
+----------------------------+
| 3|
+----------------------------+
Example 2: Counting distinct values in a single column DataFrame representing strings
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("apple",), ("orange",), ("apple",), ("banana",)], ['fruit'])
>>> df.agg(sf.approx_count_distinct("fruit")).show()
+----------------------------+
|approx_count_distinct(fruit)|
+----------------------------+
| 3|
+----------------------------+
Example 3: Counting distinct values in a DataFrame with multiple columns
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("Alice", 1), ("Alice", 2), ("Bob", 3), ("Bob", 3)], ["name", "value"])
>>> df = df.withColumn("combined", sf.struct("name", "value"))
>>> df.agg(sf.approx_count_distinct(df.combined)).show()
+-------------------------------+
|approx_count_distinct(combined)|
+-------------------------------+
| 3|
+-------------------------------+
Example 4: Counting distinct values with a specified relative standard deviation
>>> from pyspark.sql import functions as sf
>>> spark.range(100000).agg(
... sf.approx_count_distinct("id").alias('with_default_rsd'),
... sf.approx_count_distinct("id", 0.1).alias('with_rsd_0.1')
... ).show()
+----------------+------------+
|with_default_rsd|with_rsd_0.1|
+----------------+------------+
| 95546| 102065|
+----------------+------------+
"""
from pyspark.sql.classic.column import _to_java_column
if rsd is None:
return _invoke_function_over_columns("approx_count_distinct", col)
else:
return _invoke_function("approx_count_distinct", _to_java_column(col), _enum_to_value(rsd))
@_try_remote_functions
def broadcast(df: "DataFrame") -> "DataFrame":
"""
Marks a DataFrame as small enough for use in broadcast joins.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`~pyspark.sql.DataFrame`
DataFrame marked as ready for broadcast join.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([1, 2, 3, 3, 4], "int")
>>> df_small = spark.range(3)
>>> df_b = sf.broadcast(df_small)
>>> df.join(df_b, df.value == df_small.id).show()
+-----+---+
|value| id|
+-----+---+
| 1| 1|
| 2| 2|
+-----+---+
"""
from py4j.java_gateway import JVMView
from pyspark.sql.dataframe import DataFrame
sc = _get_active_spark_context()
return DataFrame(cast(JVMView, sc._jvm).functions.broadcast(df._jdf), df.sparkSession)
@_try_remote_functions
def coalesce(*cols: "ColumnOrName") -> Column:
"""Returns the first column that is not null.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : :class:`~pyspark.sql.Column` or column name
list of columns to work on.
Returns
-------
:class:`~pyspark.sql.Column`
value of the first column that is not null.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(None, None), (1, None), (None, 2)], ("a", "b"))
>>> df.show()
+----+----+
| a| b|
+----+----+
|NULL|NULL|
| 1|NULL|
|NULL| 2|
+----+----+
>>> df.select('*', sf.coalesce("a", df["b"])).show()
+----+----+--------------+
| a| b|coalesce(a, b)|
+----+----+--------------+
|NULL|NULL| NULL|
| 1|NULL| 1|
|NULL| 2| 2|
+----+----+--------------+
>>> df.select('*', sf.coalesce(df["a"], lit(0.0))).show()
+----+----+----------------+
| a| b|coalesce(a, 0.0)|
+----+----+----------------+
|NULL|NULL| 0.0|
| 1|NULL| 1.0|
|NULL| 2| 0.0|
+----+----+----------------+
"""
return _invoke_function_over_seq_of_columns("coalesce", cols)
@_try_remote_functions
def corr(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""Returns a new :class:`~pyspark.sql.Column` for the Pearson Correlation Coefficient for
``col1`` and ``col2``.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
first column to calculate correlation.
col2 : :class:`~pyspark.sql.Column` or column name
second column to calculate correlation.
Returns
-------
:class:`~pyspark.sql.Column`
Pearson Correlation Coefficient of these two column values.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> a = range(20)
>>> b = [2 * x for x in range(20)]
>>> df = spark.createDataFrame(zip(a, b), ["a", "b"])
>>> df.agg(sf.corr("a", df.b)).show()
+----------+
|corr(a, b)|
+----------+
| 1.0|
+----------+
"""
return _invoke_function_over_columns("corr", col1, col2)
@_try_remote_functions
def covar_pop(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""Returns a new :class:`~pyspark.sql.Column` for the population covariance of ``col1`` and
``col2``.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
first column to calculate covariance.
col2 : :class:`~pyspark.sql.Column` or column name
second column to calculate covariance.
Returns
-------
:class:`~pyspark.sql.Column`
covariance of these two column values.
See Also
--------
:meth:`pyspark.sql.functions.covar_samp`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> a = [1] * 10
>>> b = [1] * 10
>>> df = spark.createDataFrame(zip(a, b), ["a", "b"])
>>> df.agg(sf.covar_pop("a", df.b)).show()
+---------------+
|covar_pop(a, b)|
+---------------+
| 0.0|
+---------------+
"""
return _invoke_function_over_columns("covar_pop", col1, col2)
@_try_remote_functions
def covar_samp(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""Returns a new :class:`~pyspark.sql.Column` for the sample covariance of ``col1`` and
``col2``.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
first column to calculate covariance.
col2 : :class:`~pyspark.sql.Column` or column name
second column to calculate covariance.
Returns
-------
:class:`~pyspark.sql.Column`
sample covariance of these two column values.
See Also
--------
:meth:`pyspark.sql.functions.covar_pop`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> a = [1] * 10
>>> b = [1] * 10
>>> df = spark.createDataFrame(zip(a, b), ["a", "b"])
>>> df.agg(sf.covar_samp("a", df.b)).show()
+----------------+
|covar_samp(a, b)|
+----------------+
| 0.0|
+----------------+
"""
return _invoke_function_over_columns("covar_samp", col1, col2)
@_try_remote_functions
def countDistinct(col: "ColumnOrName", *cols: "ColumnOrName") -> Column:
"""Returns a new :class:`~pyspark.sql.Column` for distinct count of ``col`` or ``cols``.
An alias of :func:`count_distinct`, and it is encouraged to use :func:`count_distinct`
directly.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,), (1,), (3,)], ["value"])
>>> df.select(sf.count_distinct(df.value)).show()
+---------------------+
|count(DISTINCT value)|
+---------------------+
| 2|
+---------------------+
>>> df.select(sf.countDistinct(df.value)).show()
+---------------------+
|count(DISTINCT value)|
+---------------------+
| 2|
+---------------------+
"""
return count_distinct(col, *cols)
@_try_remote_functions
def count_distinct(col: "ColumnOrName", *cols: "ColumnOrName") -> Column:
"""Returns a new :class:`Column` for distinct count of ``col`` or ``cols``.
.. versionadded:: 3.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
first column to compute on.
cols : :class:`~pyspark.sql.Column` or column name
other columns to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
distinct values of these two column values.
Examples
--------
Example 1: Counting distinct values of a single column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,), (1,), (3,)], ["value"])
>>> df.select(sf.count_distinct(df.value)).show()
+---------------------+
|count(DISTINCT value)|
+---------------------+
| 2|
+---------------------+
Example 2: Counting distinct values of multiple columns
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, 1), (1, 2)], ["value1", "value2"])
>>> df.select(sf.count_distinct(df.value1, df.value2)).show()
+------------------------------+
|count(DISTINCT value1, value2)|
+------------------------------+
| 2|
+------------------------------+
Example 3: Counting distinct values with column names as strings
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, 1), (1, 2)], ["value1", "value2"])
>>> df.select(sf.count_distinct("value1", "value2")).show()
+------------------------------+
|count(DISTINCT value1, value2)|
+------------------------------+
| 2|
+------------------------------+
"""
from pyspark.sql.classic.column import _to_seq, _to_java_column
sc = _get_active_spark_context()
return _invoke_function(
"count_distinct", _to_java_column(col), _to_seq(sc, cols, _to_java_column)
)
@_try_remote_functions
def first(col: "ColumnOrName", ignorenulls: bool = False) -> Column:
"""Aggregate function: returns the first value in a group.
The function by default returns the first values it sees. It will return the first non-null
value it sees when ignoreNulls is set to true. If all values are null, then null is returned.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The function is non-deterministic because its results depends on the order of the
rows which may be non-deterministic after a shuffle.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to fetch first value for.
ignorenulls : bool
if first value is null then look for first non-null value. ``False``` by default.
Returns
-------
:class:`~pyspark.sql.Column`
first value of the group.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5), ("Alice", None)], ("name", "age"))
>>> df = df.orderBy(df.age)
>>> df.groupby("name").agg(sf.first("age")).orderBy("name").show()
+-----+----------+
| name|first(age)|
+-----+----------+
|Alice| NULL|
| Bob| 5|
+-----+----------+
To ignore any null values, set ``ignorenulls`` to `True`
>>> df.groupby("name").agg(sf.first("age", ignorenulls=True)).orderBy("name").show()
+-----+----------+
| name|first(age)|
+-----+----------+
|Alice| 2|
| Bob| 5|
+-----+----------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("first", _to_java_column(col), _enum_to_value(ignorenulls))
@_try_remote_functions
def grouping(col: "ColumnOrName") -> Column:
"""
Aggregate function: indicates whether a specified column in a GROUP BY list is aggregated
or not, returns 1 for aggregated or 0 for not aggregated in the result set.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to check if it's aggregated.
Returns
-------
:class:`~pyspark.sql.Column`
returns 1 for aggregated or 0 for not aggregated in the result set.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], ("name", "age"))
>>> df.cube("name").agg(sf.grouping("name"), sf.sum("age")).orderBy("name").show()
+-----+--------------+--------+
| name|grouping(name)|sum(age)|
+-----+--------------+--------+
| NULL| 1| 7|
|Alice| 0| 2|
| Bob| 0| 5|
+-----+--------------+--------+
"""
return _invoke_function_over_columns("grouping", col)
@_try_remote_functions
def grouping_id(*cols: "ColumnOrName") -> Column:
"""
Aggregate function: returns the level of grouping, equals to
(grouping(c1) << (n-1)) + (grouping(c2) << (n-2)) + ... + grouping(cn)
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The list of columns should match with grouping columns exactly, or empty (means all
the grouping columns).
Parameters
----------
cols : :class:`~pyspark.sql.Column` or column name
columns to check for.
Returns
-------
:class:`~pyspark.sql.Column`
returns level of the grouping it relates to.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(1, "a", "a"), (3, "a", "a"), (4, "b", "c")], ["c1", "c2", "c3"])
>>> df.cube("c2", "c3").agg(sf.grouping_id(), sf.sum("c1")).orderBy("c2", "c3").show()
+----+----+-------------+-------+
| c2| c3|grouping_id()|sum(c1)|
+----+----+-------------+-------+
|NULL|NULL| 3| 8|
|NULL| a| 2| 4|
|NULL| c| 2| 4|
| a|NULL| 1| 4|
| a| a| 0| 4|
| b|NULL| 1| 4|
| b| c| 0| 4|
+----+----+-------------+-------+
"""
return _invoke_function_over_seq_of_columns("grouping_id", cols)
@_try_remote_functions
def count_min_sketch(
col: "ColumnOrName",
eps: Union[Column, float],
confidence: Union[Column, float],
seed: Optional[Union[Column, int]] = None,
) -> Column:
"""
Returns a count-min sketch of a column with the given esp, confidence and seed.
The result is an array of bytes, which can be deserialized to a `CountMinSketch` before usage.
Count-min sketch is a probabilistic data structure used for cardinality estimation
using sub-linear space.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
eps : :class:`~pyspark.sql.Column` or float
relative error, must be positive
.. versionchanged:: 4.0.0
`eps` now accepts float value.
confidence : :class:`~pyspark.sql.Column` or float
confidence, must be positive and less than 1.0
.. versionchanged:: 4.0.0
`confidence` now accepts float value.
seed : :class:`~pyspark.sql.Column` or int, optional
random seed
.. versionchanged:: 4.0.0
`seed` now accepts int value.
Returns
-------
:class:`~pyspark.sql.Column`
count-min sketch of the column
Examples
--------
Example 1: Using columns as arguments
>>> from pyspark.sql import functions as sf
>>> spark.range(100).select(
... sf.hex(sf.count_min_sketch(sf.col("id"), sf.lit(3.0), sf.lit(0.1), sf.lit(1)))
... ).show(truncate=False)
+------------------------------------------------------------------------+
|hex(count_min_sketch(id, 3.0, 0.1, 1)) |
+------------------------------------------------------------------------+
|0000000100000000000000640000000100000001000000005D8D6AB90000000000000064|
+------------------------------------------------------------------------+
Example 2: Using numbers as arguments
>>> from pyspark.sql import functions as sf
>>> spark.range(100).select(
... sf.hex(sf.count_min_sketch("id", 1.0, 0.3, 2))
... ).show(truncate=False)
+----------------------------------------------------------------------------------------+
|hex(count_min_sketch(id, 1.0, 0.3, 2)) |
+----------------------------------------------------------------------------------------+
|0000000100000000000000640000000100000002000000005D96391C00000000000000320000000000000032|
+----------------------------------------------------------------------------------------+
Example 3: Using a long seed
>>> from pyspark.sql import functions as sf
>>> spark.range(100).select(
... sf.hex(sf.count_min_sketch("id", sf.lit(1.5), 0.2, 1111111111111111111))
... ).show(truncate=False)
+----------------------------------------------------------------------------------------+
|hex(count_min_sketch(id, 1.5, 0.2, 1111111111111111111)) |
+----------------------------------------------------------------------------------------+
|00000001000000000000006400000001000000020000000044078BA100000000000000320000000000000032|
+----------------------------------------------------------------------------------------+
Example 4: Using a random seed
>>> from pyspark.sql import functions as sf
>>> spark.range(100).select(
... sf.hex(sf.count_min_sketch("id", sf.lit(1.5), 0.6))
... ).show(truncate=False) # doctest: +SKIP
+----------------------------------------------------------------------------------------------------------------------------------------+
|hex(count_min_sketch(id, 1.5, 0.6, 2120704260)) |
+----------------------------------------------------------------------------------------------------------------------------------------+
|0000000100000000000000640000000200000002000000005ADECCEE00000000153EBE090000000000000033000000000000003100000000000000320000000000000032|
+----------------------------------------------------------------------------------------------------------------------------------------+
""" # noqa: E501
_eps = lit(eps)
_conf = lit(confidence)
if seed is None:
return _invoke_function_over_columns("count_min_sketch", col, _eps, _conf)
else:
return _invoke_function_over_columns("count_min_sketch", col, _eps, _conf, lit(seed))
@_try_remote_functions
def input_file_name() -> Column:
"""
Creates a string column for the file name of the current Spark task.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`~pyspark.sql.Column`
file names.
See Also
--------
:meth:`pyspark.sql.functions.input_file_block_length`
:meth:`pyspark.sql.functions.input_file_block_start`
Examples
--------
>>> import os
>>> from pyspark.sql import functions as sf
>>> path = os.path.abspath(__file__)
>>> df = spark.read.text(path)
>>> df.select(sf.input_file_name()).first()
Row(input_file_name()='file:///...')
"""
return _invoke_function("input_file_name")
@_try_remote_functions
def isnan(col: "ColumnOrName") -> Column:
"""An expression that returns true if the column is NaN.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
True if value is NaN and False otherwise.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1.0, float('nan')), (float('nan'), 2.0)], ("a", "b"))
>>> df.select("*", sf.isnan("a"), sf.isnan(df.b)).show()
+---+---+--------+--------+
| a| b|isnan(a)|isnan(b)|
+---+---+--------+--------+
|1.0|NaN| false| true|
|NaN|2.0| true| false|
+---+---+--------+--------+
"""
return _invoke_function_over_columns("isnan", col)
@_try_remote_functions
def isnull(col: "ColumnOrName") -> Column:
"""An expression that returns true if the column is null.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
True if value is null and False otherwise.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, None), (None, 2)], ("a", "b"))
>>> df.select("*", sf.isnull("a"), isnull(df.b)).show()
+----+----+-----------+-----------+
| a| b|(a IS NULL)|(b IS NULL)|
+----+----+-----------+-----------+
| 1|NULL| false| true|
|NULL| 2| true| false|
+----+----+-----------+-----------+
"""
return _invoke_function_over_columns("isnull", col)
@_try_remote_functions
def last(col: "ColumnOrName", ignorenulls: bool = False) -> Column:
"""Aggregate function: returns the last value in a group.
The function by default returns the last values it sees. It will return the last non-null
value it sees when ignoreNulls is set to true. If all values are null, then null is returned.
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The function is non-deterministic because its results depends on the order of the
rows which may be non-deterministic after a shuffle.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column to fetch last value for.
ignorenulls : bool
if last value is null then look for non-null value. ``False``` by default.
Returns
-------
:class:`~pyspark.sql.Column`
last value of the group.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5), ("Alice", None)], ("name", "age"))
>>> df = df.orderBy(df.age.desc())
>>> df.groupby("name").agg(sf.last("age")).orderBy("name").show()
+-----+---------+
| name|last(age)|
+-----+---------+
|Alice| NULL|
| Bob| 5|
+-----+---------+
To ignore any null values, set ``ignorenulls`` to `True`
>>> df.groupby("name").agg(sf.last("age", ignorenulls=True)).orderBy("name").show()
+-----+---------+
| name|last(age)|
+-----+---------+
|Alice| 2|
| Bob| 5|
+-----+---------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("last", _to_java_column(col), _enum_to_value(ignorenulls))
@_try_remote_functions
def monotonically_increasing_id() -> Column:
"""A column that generates monotonically increasing 64-bit integers.
The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive.
The current implementation puts the partition ID in the upper 31 bits, and the record number
within each partition in the lower 33 bits. The assumption is that the data frame has
less than 1 billion partitions, and each partition has less than 8 billion records.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The function is non-deterministic because its result depends on partition IDs.
As an example, consider a :class:`DataFrame` with two partitions, each with 3 records.
This expression would return the following IDs:
0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594.
Returns
-------
:class:`~pyspark.sql.Column`
last value of the group.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(0, 10, 1, 2).select(
... "*",
... sf.spark_partition_id(),
... sf.monotonically_increasing_id()).show()
+---+--------------------+-----------------------------+
| id|SPARK_PARTITION_ID()|monotonically_increasing_id()|
+---+--------------------+-----------------------------+
| 0| 0| 0|
| 1| 0| 1|
| 2| 0| 2|
| 3| 0| 3|
| 4| 0| 4|
| 5| 1| 8589934592|
| 6| 1| 8589934593|
| 7| 1| 8589934594|
| 8| 1| 8589934595|
| 9| 1| 8589934596|
+---+--------------------+-----------------------------+
"""
return _invoke_function("monotonically_increasing_id")
@_try_remote_functions
def nanvl(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""Returns col1 if it is not NaN, or col2 if col1 is NaN.
Both inputs should be floating point columns (:class:`DoubleType` or :class:`FloatType`).
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
first column to check.
col2 : :class:`~pyspark.sql.Column` or column name
second column to return if first is NaN.
Returns
-------
:class:`~pyspark.sql.Column`
value from first column or second if first is NaN .
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1.0, float('nan')), (float('nan'), 2.0)], ("a", "b"))
>>> df.select("*", sf.nanvl("a", "b"), sf.nanvl(df.a, df.b)).show()
+---+---+-----------+-----------+
| a| b|nanvl(a, b)|nanvl(a, b)|
+---+---+-----------+-----------+
|1.0|NaN| 1.0| 1.0|
|NaN|2.0| 2.0| 2.0|
+---+---+-----------+-----------+
"""
return _invoke_function_over_columns("nanvl", col1, col2)
@_try_remote_functions
def percentile(
col: "ColumnOrName",
percentage: Union[Column, float, Sequence[float], Tuple[float, ...]],
frequency: Union[Column, int] = 1,
) -> Column:
"""Returns the exact percentile(s) of numeric column `expr` at the given percentage(s)
with value range in [0.0, 1.0].
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
percentage : :class:`~pyspark.sql.Column`, float, list of floats or tuple of floats
percentage in decimal (must be between 0.0 and 1.0).
frequency : :class:`~pyspark.sql.Column` or int is a positive numeric literal which
controls frequency.
Returns
-------
:class:`~pyspark.sql.Column`
the exact `percentile` of the numeric column.
See Also
--------
:meth:`pyspark.sql.functions.median`
:meth:`pyspark.sql.functions.approx_percentile`
:meth:`pyspark.sql.functions.percentile_approx`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> key = (sf.col("id") % 3).alias("key")
>>> value = (sf.randn(42) + key * 10).alias("value")
>>> df = spark.range(0, 1000, 1, 1).select(key, value)
>>> df.select(
... sf.percentile("value", [0.25, 0.5, 0.75], sf.lit(1))
... ).show(truncate=False)
+--------------------------------------------------------+
|percentile(value, array(0.25, 0.5, 0.75), 1) |
+--------------------------------------------------------+
|[0.7441991494121..., 9.9900713756..., 19.33740203080...]|
+--------------------------------------------------------+
>>> df.groupBy("key").agg(
... sf.percentile("value", sf.lit(0.5), sf.lit(1))
... ).sort("key").show()
+---+-------------------------+
|key|percentile(value, 0.5, 1)|
+---+-------------------------+
| 0| -0.03449962216667901|
| 1| 9.990389751837329|
| 2| 19.967859769284075|
+---+-------------------------+
"""
percentage = lit(list(percentage)) if isinstance(percentage, (list, tuple)) else lit(percentage)
return _invoke_function_over_columns("percentile", col, percentage, lit(frequency))
@_try_remote_functions
def percentile_approx(
col: "ColumnOrName",
percentage: Union[Column, float, Sequence[float], Tuple[float, ...]],
accuracy: Union[Column, int] = 10000,
) -> Column:
"""Returns the approximate `percentile` of the numeric column `col` which is the smallest value
in the ordered `col` values (sorted from least to greatest) such that no more than `percentage`
of `col` values is less than the value or equal to that value.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column.
percentage : :class:`~pyspark.sql.Column`, float, list of floats or tuple of floats
percentage in decimal (must be between 0.0 and 1.0).
When percentage is an array, each value of the percentage array must be between 0.0 and 1.0.
In this case, returns the approximate percentile array of column col
at the given percentage array.
accuracy : :class:`~pyspark.sql.Column` or int
is a positive numeric literal which controls approximation accuracy
at the cost of memory. Higher value of accuracy yields better accuracy,
1.0/accuracy is the relative error of the approximation. (default: 10000).
Returns
-------
:class:`~pyspark.sql.Column`
approximate `percentile` of the numeric column.
See Also
--------
:meth:`pyspark.sql.functions.median`
:meth:`pyspark.sql.functions.percentile`
:meth:`pyspark.sql.functions.approx_percentile`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> key = (sf.col("id") % 3).alias("key")
>>> value = (sf.randn(42) + key * 10).alias("value")
>>> df = spark.range(0, 1000, 1, 1).select(key, value)
>>> df.select(
... sf.percentile_approx("value", [0.25, 0.5, 0.75], 1000000)
... ).show(truncate=False)
+----------------------------------------------------------+
|percentile_approx(value, array(0.25, 0.5, 0.75), 1000000) |
+----------------------------------------------------------+
|[0.7264430125286..., 9.98975299938..., 19.335304783039...]|
+----------------------------------------------------------+
>>> df.groupBy("key").agg(
... sf.percentile_approx("value", sf.lit(0.5), sf.lit(1000000))
... ).sort("key").show()
+---+--------------------------------------+
|key|percentile_approx(value, 0.5, 1000000)|
+---+--------------------------------------+
| 0| -0.03519435193070...|
| 1| 9.990389751837...|
| 2| 19.967859769284...|
+---+--------------------------------------+
"""
percentage = lit(list(percentage)) if isinstance(percentage, (list, tuple)) else lit(percentage)
return _invoke_function_over_columns("percentile_approx", col, percentage, lit(accuracy))
@_try_remote_functions
def approx_percentile(
col: "ColumnOrName",
percentage: Union[Column, float, Sequence[float], Tuple[float, ...]],
accuracy: Union[Column, int] = 10000,
) -> Column:
"""Returns the approximate `percentile` of the numeric column `col` which is the smallest value
in the ordered `col` values (sorted from least to greatest) such that no more than `percentage`
of `col` values is less than the value or equal to that value.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column.
percentage : :class:`~pyspark.sql.Column`, float, list of floats or tuple of floats
percentage in decimal (must be between 0.0 and 1.0).
When percentage is an array, each value of the percentage array must be between 0.0 and 1.0.
In this case, returns the approximate percentile array of column col
at the given percentage array.
accuracy : :class:`~pyspark.sql.Column` or int
is a positive numeric literal which controls approximation accuracy
at the cost of memory. Higher value of accuracy yields better accuracy,
1.0/accuracy is the relative error of the approximation. (default: 10000).
Returns
-------
:class:`~pyspark.sql.Column`
approximate `percentile` of the numeric column.
See Also
--------
:meth:`pyspark.sql.functions.median`
:meth:`pyspark.sql.functions.percentile`
:meth:`pyspark.sql.functions.percentile_approx`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> key = (sf.col("id") % 3).alias("key")
>>> value = (sf.randn(42) + key * 10).alias("value")
>>> df = spark.range(0, 1000, 1, 1).select(key, value)
>>> df.select(
... sf.approx_percentile("value", [0.25, 0.5, 0.75], 1000000)
... ).show(truncate=False)
+----------------------------------------------------------+
|approx_percentile(value, array(0.25, 0.5, 0.75), 1000000) |
+----------------------------------------------------------+
|[0.7264430125286..., 9.98975299938..., 19.335304783039...]|
+----------------------------------------------------------+
>>> df.groupBy("key").agg(
... sf.approx_percentile("value", sf.lit(0.5), sf.lit(1000000))
... ).sort("key").show()
+---+--------------------------------------+
|key|approx_percentile(value, 0.5, 1000000)|
+---+--------------------------------------+
| 0| -0.03519435193070...|
| 1| 9.990389751837...|
| 2| 19.967859769284...|
+---+--------------------------------------+
"""
percentage = lit(list(percentage)) if isinstance(percentage, (list, tuple)) else lit(percentage)
return _invoke_function_over_columns("approx_percentile", col, percentage, lit(accuracy))
@_try_remote_functions
def rand(seed: Optional[int] = None) -> Column:
"""Generates a random column with independent and identically distributed (i.i.d.) samples
uniformly distributed in [0.0, 1.0).
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The function is non-deterministic in general case.
Parameters
----------
seed : int, optional
Seed value for the random generator.
Returns
-------
:class:`~pyspark.sql.Column`
A column of random values.
See Also
--------
:meth:`pyspark.sql.functions.randn`
:meth:`pyspark.sql.functions.randstr`
:meth:`pyspark.sql.functions.uniform`
Examples
--------
Example 1: Generate a random column without a seed
>>> from pyspark.sql import functions as sf
>>> spark.range(0, 2, 1, 1).select("*", sf.rand()).show() # doctest: +SKIP
+---+-------------------------+
| id|rand(-158884697681280011)|
+---+-------------------------+
| 0| 0.9253464547887...|
| 1| 0.6533254118758...|
+---+-------------------------+
Example 2: Generate a random column with a specific seed
>>> spark.range(0, 2, 1, 1).select("*", sf.rand(seed=42)).show()
+---+------------------+
| id| rand(42)|
+---+------------------+
| 0| 0.619189370225...|
| 1|0.5096018842446...|
+---+------------------+
"""
if seed is not None:
return _invoke_function("rand", _enum_to_value(seed))
else:
return _invoke_function("rand")
random = rand
@_try_remote_functions
def randn(seed: Optional[int] = None) -> Column:
"""Generates a random column with independent and identically distributed (i.i.d.) samples
from the standard normal distribution.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The function is non-deterministic in general case.
Parameters
----------
seed : int (default: None)
Seed value for the random generator.
Returns
-------
:class:`~pyspark.sql.Column`
A column of random values.
See Also
--------
:meth:`pyspark.sql.functions.rand`
:meth:`pyspark.sql.functions.randstr`
:meth:`pyspark.sql.functions.uniform`
Examples
--------
Example 1: Generate a random column without a seed
>>> from pyspark.sql import functions as sf
>>> spark.range(0, 2, 1, 1).select("*", sf.randn()).show() # doctest: +SKIP
+---+--------------------------+
| id|randn(3968742514375399317)|
+---+--------------------------+
| 0| -0.47968645355788...|
| 1| -0.4950952457305...|
+---+--------------------------+
Example 2: Generate a random column with a specific seed
>>> spark.range(0, 2, 1, 1).select("*", sf.randn(seed=42)).show()
+---+------------------+
| id| randn(42)|
+---+------------------+
| 0| 2.384479054241...|
| 1|0.1920934041293...|
+---+------------------+
"""
if seed is not None:
return _invoke_function("randn", _enum_to_value(seed))
else:
return _invoke_function("randn")
@_try_remote_functions
def round(col: "ColumnOrName", scale: Optional[Union[Column, int]] = None) -> Column:
"""
Round the given value to `scale` decimal places using HALF_UP rounding mode if `scale` >= 0
or at integral part when `scale` < 0.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column or column name to compute the round on.
scale : :class:`~pyspark.sql.Column` or int, optional
An optional parameter to control the rounding behavior.
.. versionchanged:: 4.0.0
Support Column type.
Returns
-------
:class:`~pyspark.sql.Column`
A column for the rounded value.
Examples
--------
Example 1: Compute the rounded of a column value
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.round(sf.lit(2.5))).show()
+-------------+
|round(2.5, 0)|
+-------------+
| 3.0|
+-------------+
Example 2: Compute the rounded of a column value with a specified scale
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.round(sf.lit(2.1267), sf.lit(2))).show()
+----------------+
|round(2.1267, 2)|
+----------------+
| 2.13|
+----------------+
"""
if scale is None:
return _invoke_function_over_columns("round", col)
else:
scale = _enum_to_value(scale)
scale = lit(scale) if isinstance(scale, int) else scale
return _invoke_function_over_columns("round", col, scale) # type: ignore[arg-type]
@_try_remote_functions
def bround(col: "ColumnOrName", scale: Optional[Union[Column, int]] = None) -> Column:
"""
Round the given value to `scale` decimal places using HALF_EVEN rounding mode if `scale` >= 0
or at integral part when `scale` < 0.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The target column or column name to compute the round on.
scale : :class:`~pyspark.sql.Column` or int, optional
An optional parameter to control the rounding behavior.
.. versionchanged:: 4.0.0
Support Column type.
Returns
-------
:class:`~pyspark.sql.Column`
A column for the rounded value.
Examples
--------
Example 1: Compute the rounded of a column value
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.bround(sf.lit(2.5))).show()
+--------------+
|bround(2.5, 0)|
+--------------+
| 2.0|
+--------------+
Example 2: Compute the rounded of a column value with a specified scale
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.bround(sf.lit(2.1267), sf.lit(2))).show()
+-----------------+
|bround(2.1267, 2)|
+-----------------+
| 2.13|
+-----------------+
"""
if scale is None:
return _invoke_function_over_columns("bround", col)
else:
scale = _enum_to_value(scale)
scale = lit(scale) if isinstance(scale, int) else scale
return _invoke_function_over_columns("bround", col, scale) # type: ignore[arg-type]
@_try_remote_functions
def shiftLeft(col: "ColumnOrName", numBits: int) -> Column:
"""Shift the given value numBits left.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 3.2.0
Use :func:`shiftleft` instead.
"""
warnings.warn("Deprecated in 3.2, use shiftleft instead.", FutureWarning)
return shiftleft(col, numBits)
@_try_remote_functions
def shiftleft(col: "ColumnOrName", numBits: int) -> Column:
"""Shift the given value numBits left.
.. versionadded:: 3.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column of values to shift.
numBits : int
number of bits to shift.
Returns
-------
:class:`~pyspark.sql.Column`
shifted value.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(4).select("*", sf.shiftleft('id', 1)).show()
+---+----------------+
| id|shiftleft(id, 1)|
+---+----------------+
| 0| 0|
| 1| 2|
| 2| 4|
| 3| 6|
+---+----------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("shiftleft", _to_java_column(col), _enum_to_value(numBits))
@_try_remote_functions
def shiftRight(col: "ColumnOrName", numBits: int) -> Column:
"""(Signed) shift the given value numBits right.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 3.2.0
Use :func:`shiftright` instead.
"""
warnings.warn("Deprecated in 3.2, use shiftright instead.", FutureWarning)
return shiftright(col, numBits)
@_try_remote_functions
def shiftright(col: "ColumnOrName", numBits: int) -> Column:
"""(Signed) shift the given value numBits right.
.. versionadded:: 3.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column of values to shift.
numBits : int
number of bits to shift.
Returns
-------
:class:`~pyspark.sql.Column`
shifted values.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(4).select("*", sf.shiftright('id', 1)).show()
+---+-----------------+
| id|shiftright(id, 1)|
+---+-----------------+
| 0| 0|
| 1| 0|
| 2| 1|
| 3| 1|
+---+-----------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("shiftright", _to_java_column(col), _enum_to_value(numBits))
@_try_remote_functions
def shiftRightUnsigned(col: "ColumnOrName", numBits: int) -> Column:
"""Unsigned shift the given value numBits right.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 3.2.0
Use :func:`shiftrightunsigned` instead.
"""
warnings.warn("Deprecated in 3.2, use shiftrightunsigned instead.", FutureWarning)
return shiftrightunsigned(col, numBits)
@_try_remote_functions
def shiftrightunsigned(col: "ColumnOrName", numBits: int) -> Column:
"""Unsigned shift the given value numBits right.
.. versionadded:: 3.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column of values to shift.
numBits : int
number of bits to shift.
Returns
-------
:class:`~pyspark.sql.Column`
shifted value.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(4).select("*", sf.shiftrightunsigned(sf.col('id') - 2, 1)).show()
+---+-------------------------------+
| id|shiftrightunsigned((id - 2), 1)|
+---+-------------------------------+
| 0| 9223372036854775807|
| 1| 9223372036854775807|
| 2| 0|
| 3| 0|
+---+-------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("shiftrightunsigned", _to_java_column(col), _enum_to_value(numBits))
@_try_remote_functions
def spark_partition_id() -> Column:
"""A column for partition ID.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
This is non deterministic because it depends on data partitioning and task scheduling.
Returns
-------
:class:`~pyspark.sql.Column`
partition id the record belongs to.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(10, numPartitions=5).select("*", sf.spark_partition_id()).show()
+---+--------------------+
| id|SPARK_PARTITION_ID()|
+---+--------------------+
| 0| 0|
| 1| 0|
| 2| 1|
| 3| 1|
| 4| 2|
| 5| 2|
| 6| 3|
| 7| 3|
| 8| 4|
| 9| 4|
+---+--------------------+
"""
return _invoke_function("spark_partition_id")
@_try_remote_functions
def expr(str: str) -> Column:
"""Parses the expression string into the column that it represents
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
str : expression string
expression defined in string.
Returns
-------
:class:`~pyspark.sql.Column`
column representing the expression.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([["Alice"], ["Bob"]], ["name"])
>>> df.select("*", sf.expr("length(name)")).show()
+-----+------------+
| name|length(name)|
+-----+------------+
|Alice| 5|
| Bob| 3|
+-----+------------+
"""
return _invoke_function("expr", str)
@overload
def struct(*cols: "ColumnOrName") -> Column:
...
@overload
def struct(__cols: Union[Sequence["ColumnOrName"], Tuple["ColumnOrName", ...]]) -> Column:
...
@_try_remote_functions
def struct(
*cols: Union["ColumnOrName", Union[Sequence["ColumnOrName"], Tuple["ColumnOrName", ...]]]
) -> Column:
"""Creates a new struct column.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : list, set, :class:`~pyspark.sql.Column` or column name
column names or :class:`~pyspark.sql.Column`\\s to contain in the output struct.
Returns
-------
:class:`~pyspark.sql.Column`
a struct type column of given columns.
See Also
--------
:meth:`pyspark.sql.functions.named_struct`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], ("name", "age"))
>>> df.select("*", sf.struct('age', df.name)).show()
+-----+---+-----------------+
| name|age|struct(age, name)|
+-----+---+-----------------+
|Alice| 2| {2, Alice}|
| Bob| 5| {5, Bob}|
+-----+---+-----------------+
"""
if len(cols) == 1 and isinstance(cols[0], (list, set)):
cols = cols[0] # type: ignore[assignment]
return _invoke_function_over_seq_of_columns("struct", cols) # type: ignore[arg-type]
@_try_remote_functions
def named_struct(*cols: "ColumnOrName") -> Column:
"""
Creates a struct with the given field names and values.
.. versionadded:: 3.5.0
Parameters
----------
cols : :class:`~pyspark.sql.Column` or column name
list of columns to work on.
Returns
-------
:class:`~pyspark.sql.Column`
See Also
--------
:meth:`pyspark.sql.functions.struct`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1, 2)], ['a', 'b'])
>>> df.select("*", sf.named_struct(sf.lit('x'), df.a, sf.lit('y'), "b")).show()
+---+---+------------------------+
| a| b|named_struct(x, a, y, b)|
+---+---+------------------------+
| 1| 2| {1, 2}|
+---+---+------------------------+
"""
return _invoke_function_over_seq_of_columns("named_struct", cols)
@_try_remote_functions
def greatest(*cols: "ColumnOrName") -> Column:
"""
Returns the greatest value of the list of column names, skipping null values.
This function takes at least 2 parameters. It will return null if all parameters are null.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols: :class:`~pyspark.sql.Column` or column name
columns to check for greatest value.
Returns
-------
:class:`~pyspark.sql.Column`
greatest value.
See Also
--------
:meth:`pyspark.sql.functions.least`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1, 4, 3)], ['a', 'b', 'c'])
>>> df.select("*", sf.greatest(df.a, "b", df.c)).show()
+---+---+---+-----------------+
| a| b| c|greatest(a, b, c)|
+---+---+---+-----------------+
| 1| 4| 3| 4|
+---+---+---+-----------------+
"""
if len(cols) < 2:
raise PySparkValueError(
errorClass="WRONG_NUM_COLUMNS",
messageParameters={"func_name": "greatest", "num_cols": "2"},
)
return _invoke_function_over_seq_of_columns("greatest", cols)
@_try_remote_functions
def least(*cols: "ColumnOrName") -> Column:
"""
Returns the least value of the list of column names, skipping null values.
This function takes at least 2 parameters. It will return null if all parameters are null.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : :class:`~pyspark.sql.Column` or column name
column names or columns to be compared
Returns
-------
:class:`~pyspark.sql.Column`
least value.
See Also
--------
:meth:`pyspark.sql.functions.greatest`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1, 4, 3)], ['a', 'b', 'c'])
>>> df.select("*", sf.least(df.a, "b", df.c)).show()
+---+---+---+--------------+
| a| b| c|least(a, b, c)|
+---+---+---+--------------+
| 1| 4| 3| 1|
+---+---+---+--------------+
"""
if len(cols) < 2:
raise PySparkValueError(
errorClass="WRONG_NUM_COLUMNS",
messageParameters={"func_name": "least", "num_cols": "2"},
)
return _invoke_function_over_seq_of_columns("least", cols)
@_try_remote_functions
def when(condition: Column, value: Any) -> Column:
"""Evaluates a list of conditions and returns one of multiple possible result expressions.
If :func:`pyspark.sql.Column.otherwise` is not invoked, None is returned for unmatched
conditions.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
condition : :class:`~pyspark.sql.Column`
a boolean :class:`~pyspark.sql.Column` expression.
value :
a literal value, or a :class:`~pyspark.sql.Column` expression.
Returns
-------
:class:`~pyspark.sql.Column`
column representing when expression.
See Also
--------
:meth:`pyspark.sql.Column.when`
:meth:`pyspark.sql.Column.otherwise`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.range(3)
>>> df.select("*", sf.when(df['id'] == 2, 3).otherwise(4)).show()
+---+------------------------------------+
| id|CASE WHEN (id = 2) THEN 3 ELSE 4 END|
+---+------------------------------------+
| 0| 4|
| 1| 4|
| 2| 3|
+---+------------------------------------+
>>> df.select("*", sf.when(df.id == 2, df.id + 1)).show()
+---+------------------------------------+
| id|CASE WHEN (id = 2) THEN (id + 1) END|
+---+------------------------------------+
| 0| NULL|
| 1| NULL|
| 2| 3|
+---+------------------------------------+
"""
# Explicitly not using ColumnOrName type here to make reading condition less opaque
if not isinstance(condition, Column):
raise PySparkTypeError(
errorClass="NOT_COLUMN",
messageParameters={"arg_name": "condition", "arg_type": type(condition).__name__},
)
value = _enum_to_value(value)
v = value._jc if isinstance(value, Column) else _enum_to_value(value)
return _invoke_function("when", condition._jc, v)
@overload # type: ignore[no-redef]
def log(arg1: "ColumnOrName") -> Column:
...
@overload
def log(arg1: float, arg2: "ColumnOrName") -> Column:
...
@_try_remote_functions
def log(arg1: Union["ColumnOrName", float], arg2: Optional["ColumnOrName"] = None) -> Column:
"""Returns the first argument-based logarithm of the second argument.
If there is only one argument, then this takes the natural logarithm of the argument.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
arg1 : :class:`~pyspark.sql.Column`, str or float
base number or actual number (in this case base is `e`)
arg2 : :class:`~pyspark.sql.Column`, str or float, optional
number to calculate logariphm for.
Returns
-------
:class:`~pyspark.sql.Column`
logariphm of given value.
Examples
--------
Example 1: Specify both base number and the input value
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1), (2), (4) AS t(value)")
>>> df.select("*", sf.log(2.0, df.value)).show()
+-----+---------------+
|value|LOG(2.0, value)|
+-----+---------------+
| 1| 0.0|
| 2| 1.0|
| 4| 2.0|
+-----+---------------+
Example 2: Return NULL for invalid input values
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1), (2), (0), (-1), (NULL) AS t(value)")
>>> df.select("*", sf.log(3.0, df.value)).show()
+-----+------------------+
|value| LOG(3.0, value)|
+-----+------------------+
| 1| 0.0|
| 2|0.6309297535714...|
| 0| NULL|
| -1| NULL|
| NULL| NULL|
+-----+------------------+
Example 3: Specify only the input value (Natural logarithm)
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1), (2), (4) AS t(value)")
>>> df.select("*", sf.log(df.value)).show()
+-----+------------------+
|value| ln(value)|
+-----+------------------+
| 1| 0.0|
| 2|0.6931471805599...|
| 4|1.3862943611198...|
+-----+------------------+
"""
from pyspark.sql.classic.column import _to_java_column
if arg2 is None:
return _invoke_function_over_columns("log", cast("ColumnOrName", arg1))
else:
return _invoke_function("log", _enum_to_value(arg1), _to_java_column(arg2))
@_try_remote_functions
def ln(col: "ColumnOrName") -> Column:
"""Returns the natural logarithm of the argument.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
a column to calculate logariphm for.
Returns
-------
:class:`~pyspark.sql.Column`
natural logarithm of given value.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(10).select("*", sf.ln('id')).show()
+---+------------------+
| id| ln(id)|
+---+------------------+
| 0| NULL|
| 1| 0.0|
| 2|0.6931471805599...|
| 3|1.0986122886681...|
| 4|1.3862943611198...|
| 5|1.6094379124341...|
| 6| 1.791759469228...|
| 7|1.9459101490553...|
| 8|2.0794415416798...|
| 9|2.1972245773362...|
+---+------------------+
"""
return _invoke_function_over_columns("ln", col)
@_try_remote_functions
def log2(col: "ColumnOrName") -> Column:
"""Returns the base-2 logarithm of the argument.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
a column to calculate logariphm for.
Returns
-------
:class:`~pyspark.sql.Column`
logariphm of given value.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(10).select("*", sf.log2('id')).show()
+---+------------------+
| id| LOG2(id)|
+---+------------------+
| 0| NULL|
| 1| 0.0|
| 2| 1.0|
| 3| 1.584962500721...|
| 4| 2.0|
| 5| 2.321928094887...|
| 6| 2.584962500721...|
| 7| 2.807354922057...|
| 8| 3.0|
| 9|3.1699250014423...|
+---+------------------+
"""
return _invoke_function_over_columns("log2", col)
@_try_remote_functions
def conv(col: "ColumnOrName", fromBase: int, toBase: int) -> Column:
"""
Convert a number in a string column from one base to another.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
a column to convert base for.
fromBase: int
from base number.
toBase: int
to base number.
Returns
-------
:class:`~pyspark.sql.Column`
logariphm of given value.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("010101",), ( "101",), ("001",)], ['n'])
>>> df.select("*", sf.conv(df.n, 2, 16)).show()
+------+--------------+
| n|conv(n, 2, 16)|
+------+--------------+
|010101| 15|
| 101| 5|
| 001| 1|
+------+--------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"conv", _to_java_column(col), _enum_to_value(fromBase), _enum_to_value(toBase)
)
@_try_remote_functions
def factorial(col: "ColumnOrName") -> Column:
"""
Computes the factorial of the given value.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
a column to calculate factorial for.
Returns
-------
:class:`~pyspark.sql.Column`
factorial of given value.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(10).select("*", sf.factorial('id')).show()
+---+-------------+
| id|factorial(id)|
+---+-------------+
| 0| 1|
| 1| 1|
| 2| 2|
| 3| 6|
| 4| 24|
| 5| 120|
| 6| 720|
| 7| 5040|
| 8| 40320|
| 9| 362880|
+---+-------------+
"""
return _invoke_function_over_columns("factorial", col)
# --------------- Window functions ------------------------
@_try_remote_functions
def lag(col: "ColumnOrName", offset: int = 1, default: Optional[Any] = None) -> Column:
"""
Window function: returns the value that is `offset` rows before the current row, and
`default` if there is less than `offset` rows before the current row. For example,
an `offset` of one will return the previous row at any given point in the window partition.
This is equivalent to the LAG function in SQL.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
name of column or expression
offset : int, optional default 1
number of row to extend
default : optional
default value
Returns
-------
:class:`~pyspark.sql.Column`
value before current row based on `offset`.
See Also
--------
:meth:`pyspark.sql.functions.lead`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame(
... [("a", 1), ("a", 2), ("a", 3), ("b", 8), ("b", 2)], ["c1", "c2"])
>>> df.show()
+---+---+
| c1| c2|
+---+---+
| a| 1|
| a| 2|
| a| 3|
| b| 8|
| b| 2|
+---+---+
>>> w = Window.partitionBy("c1").orderBy("c2")
>>> df.withColumn("previous_value", sf.lag("c2").over(w)).show()
+---+---+--------------+
| c1| c2|previous_value|
+---+---+--------------+
| a| 1| NULL|
| a| 2| 1|
| a| 3| 2|
| b| 2| NULL|
| b| 8| 2|
+---+---+--------------+
>>> df.withColumn("previous_value", sf.lag("c2", 1, 0).over(w)).show()
+---+---+--------------+
| c1| c2|previous_value|
+---+---+--------------+
| a| 1| 0|
| a| 2| 1|
| a| 3| 2|
| b| 2| 0|
| b| 8| 2|
+---+---+--------------+
>>> df.withColumn("previous_value", sf.lag("c2", 2, -1).over(w)).show()
+---+---+--------------+
| c1| c2|previous_value|
+---+---+--------------+
| a| 1| -1|
| a| 2| -1|
| a| 3| 1|
| b| 2| -1|
| b| 8| -1|
+---+---+--------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"lag", _to_java_column(col), _enum_to_value(offset), _enum_to_value(default)
)
@_try_remote_functions
def lead(col: "ColumnOrName", offset: int = 1, default: Optional[Any] = None) -> Column:
"""
Window function: returns the value that is `offset` rows after the current row, and
`default` if there is less than `offset` rows after the current row. For example,
an `offset` of one will return the next row at any given point in the window partition.
This is equivalent to the LEAD function in SQL.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
name of column or expression
offset : int, optional default 1
number of row to extend
default : optional
default value
Returns
-------
:class:`~pyspark.sql.Column`
value after current row based on `offset`.
See Also
--------
:meth:`pyspark.sql.functions.lag`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame(
... [("a", 1), ("a", 2), ("a", 3), ("b", 8), ("b", 2)], ["c1", "c2"])
>>> df.show()
+---+---+
| c1| c2|
+---+---+
| a| 1|
| a| 2|
| a| 3|
| b| 8|
| b| 2|
+---+---+
>>> w = Window.partitionBy("c1").orderBy("c2")
>>> df.withColumn("next_value", sf.lead("c2").over(w)).show()
+---+---+----------+
| c1| c2|next_value|
+---+---+----------+
| a| 1| 2|
| a| 2| 3|
| a| 3| NULL|
| b| 2| 8|
| b| 8| NULL|
+---+---+----------+
>>> df.withColumn("next_value", sf.lead("c2", 1, 0).over(w)).show()
+---+---+----------+
| c1| c2|next_value|
+---+---+----------+
| a| 1| 2|
| a| 2| 3|
| a| 3| 0|
| b| 2| 8|
| b| 8| 0|
+---+---+----------+
>>> df.withColumn("next_value", sf.lead("c2", 2, -1).over(w)).show()
+---+---+----------+
| c1| c2|next_value|
+---+---+----------+
| a| 1| 3|
| a| 2| -1|
| a| 3| -1|
| b| 2| -1|
| b| 8| -1|
+---+---+----------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"lead", _to_java_column(col), _enum_to_value(offset), _enum_to_value(default)
)
@_try_remote_functions
def nth_value(col: "ColumnOrName", offset: int, ignoreNulls: Optional[bool] = False) -> Column:
"""
Window function: returns the value that is the `offset`\\th row of the window frame
(counting from 1), and `null` if the size of window frame is less than `offset` rows.
It will return the `offset`\\th non-null value it sees when `ignoreNulls` is set to
true. If all values are null, then null is returned.
This is equivalent to the nth_value function in SQL.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
name of column or expression
offset : int
number of row to use as the value
ignoreNulls : bool, optional
indicates the Nth value should skip null in the
determination of which row to use
Returns
-------
:class:`~pyspark.sql.Column`
value of nth row.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame(
... [("a", 1), ("a", 2), ("a", 3), ("b", 8), ("b", 2)], ["c1", "c2"])
>>> df.show()
+---+---+
| c1| c2|
+---+---+
| a| 1|
| a| 2|
| a| 3|
| b| 8|
| b| 2|
+---+---+
>>> w = Window.partitionBy("c1").orderBy("c2")
>>> df.withColumn("nth_value", sf.nth_value("c2", 1).over(w)).show()
+---+---+---------+
| c1| c2|nth_value|
+---+---+---------+
| a| 1| 1|
| a| 2| 1|
| a| 3| 1|
| b| 2| 2|
| b| 8| 2|
+---+---+---------+
>>> df.withColumn("nth_value", sf.nth_value("c2", 2).over(w)).show()
+---+---+---------+
| c1| c2|nth_value|
+---+---+---------+
| a| 1| NULL|
| a| 2| 2|
| a| 3| 2|
| b| 2| NULL|
| b| 8| 8|
+---+---+---------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"nth_value", _to_java_column(col), _enum_to_value(offset), _enum_to_value(ignoreNulls)
)
@_try_remote_functions
def any_value(col: "ColumnOrName", ignoreNulls: Optional[Union[bool, Column]] = None) -> Column:
"""Returns some value of `col` for a group of rows.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
ignoreNulls : :class:`~pyspark.sql.Column` or bool, optional
if first value is null then look for first non-null value.
Returns
-------
:class:`~pyspark.sql.Column`
some value of `col` for a group of rows.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(None, 1), ("a", 2), ("a", 3), ("b", 8), ("b", 2)], ["c1", "c2"])
>>> df.select(sf.any_value('c1'), sf.any_value('c2')).show()
+-------------+-------------+
|any_value(c1)|any_value(c2)|
+-------------+-------------+
| NULL| 1|
+-------------+-------------+
>>> df.select(sf.any_value('c1', True), sf.any_value('c2', True)).show()
+-------------+-------------+
|any_value(c1)|any_value(c2)|
+-------------+-------------+
| a| 1|
+-------------+-------------+
"""
if ignoreNulls is None:
return _invoke_function_over_columns("any_value", col)
else:
ignoreNulls = _enum_to_value(ignoreNulls)
ignoreNulls = lit(ignoreNulls) if isinstance(ignoreNulls, bool) else ignoreNulls
return _invoke_function_over_columns(
"any_value", col, ignoreNulls # type: ignore[arg-type]
)
@_try_remote_functions
def first_value(col: "ColumnOrName", ignoreNulls: Optional[Union[bool, Column]] = None) -> Column:
"""Returns the first value of `col` for a group of rows. It will return the first non-null
value it sees when `ignoreNulls` is set to true. If all values are null, then null is returned.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
ignoreNulls : :class:`~pyspark.sql.Column` or bool, optional
if first value is null then look for first non-null value.
Returns
-------
:class:`~pyspark.sql.Column`
some value of `col` for a group of rows.
See Also
--------
:meth:`pyspark.sql.functions.last_value`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [(None, 1), ("a", 2), ("a", 3), ("b", 8), ("b", 2)], ["a", "b"]
... ).select(sf.first_value('a'), sf.first_value('b')).show()
+--------------+--------------+
|first_value(a)|first_value(b)|
+--------------+--------------+
| NULL| 1|
+--------------+--------------+
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [(None, 1), ("a", 2), ("a", 3), ("b", 8), ("b", 2)], ["a", "b"]
... ).select(sf.first_value('a', True), sf.first_value('b', True)).show()
+--------------+--------------+
|first_value(a)|first_value(b)|
+--------------+--------------+
| a| 1|
+--------------+--------------+
"""
if ignoreNulls is None:
return _invoke_function_over_columns("first_value", col)
else:
ignoreNulls = _enum_to_value(ignoreNulls)
ignoreNulls = lit(ignoreNulls) if isinstance(ignoreNulls, bool) else ignoreNulls
return _invoke_function_over_columns(
"first_value", col, ignoreNulls # type: ignore[arg-type]
)
@_try_remote_functions
def last_value(col: "ColumnOrName", ignoreNulls: Optional[Union[bool, Column]] = None) -> Column:
"""Returns the last value of `col` for a group of rows. It will return the last non-null
value it sees when `ignoreNulls` is set to true. If all values are null, then null is returned.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
ignoreNulls : :class:`~pyspark.sql.Column` or bool, optional
if first value is null then look for first non-null value.
Returns
-------
:class:`~pyspark.sql.Column`
some value of `col` for a group of rows.
See Also
--------
:meth:`pyspark.sql.functions.first_value`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [("a", 1), ("a", 2), ("a", 3), ("b", 8), (None, 2)], ["a", "b"]
... ).select(sf.last_value('a'), sf.last_value('b')).show()
+-------------+-------------+
|last_value(a)|last_value(b)|
+-------------+-------------+
| NULL| 2|
+-------------+-------------+
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [("a", 1), ("a", 2), ("a", 3), ("b", 8), (None, 2)], ["a", "b"]
... ).select(sf.last_value('a', True), sf.last_value('b', True)).show()
+-------------+-------------+
|last_value(a)|last_value(b)|
+-------------+-------------+
| b| 2|
+-------------+-------------+
"""
if ignoreNulls is None:
return _invoke_function_over_columns("last_value", col)
else:
ignoreNulls = _enum_to_value(ignoreNulls)
ignoreNulls = lit(ignoreNulls) if isinstance(ignoreNulls, bool) else ignoreNulls
return _invoke_function_over_columns(
"last_value", col, ignoreNulls # type: ignore[arg-type]
)
@_try_remote_functions
def count_if(col: "ColumnOrName") -> Column:
"""
Aggregate function: Returns the number of `TRUE` values for the `col`.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
the number of `TRUE` values for the `col`.
See Also
--------
:meth:`pyspark.sql.functions.count`
Examples
--------
Example 1: Counting the number of even numbers in a numeric column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("a", 1), ("a", 2), ("a", 3), ("b", 8), ("b", 2)], ["c1", "c2"])
>>> df.select(sf.count_if(sf.col('c2') % 2 == 0)).show()
+------------------------+
|count_if(((c2 % 2) = 0))|
+------------------------+
| 3|
+------------------------+
Example 2: Counting the number of rows where a string column starts with a certain letter
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("apple",), ("banana",), ("cherry",), ("apple",), ("banana",)], ["fruit"])
>>> df.select(sf.count_if(sf.col('fruit').startswith('a'))).show()
+------------------------------+
|count_if(startswith(fruit, a))|
+------------------------------+
| 2|
+------------------------------+
Example 3: Counting the number of rows where a numeric column is greater than a certain value
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,), (2,), (3,), (4,), (5,)], ["num"])
>>> df.select(sf.count_if(sf.col('num') > 3)).show()
+-------------------+
|count_if((num > 3))|
+-------------------+
| 2|
+-------------------+
Example 4: Counting the number of rows where a boolean column is True
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(True,), (False,), (True,), (False,), (True,)], ["b"])
>>> df.select(sf.count('b'), sf.count_if('b')).show()
+--------+-----------+
|count(b)|count_if(b)|
+--------+-----------+
| 5| 3|
+--------+-----------+
"""
return _invoke_function_over_columns("count_if", col)
@_try_remote_functions
def histogram_numeric(col: "ColumnOrName", nBins: Column) -> Column:
"""Computes a histogram on numeric 'col' using nb bins.
The return value is an array of (x,y) pairs representing the centers of the
histogram's bins. As the value of 'nb' is increased, the histogram approximation
gets finer-grained, but may yield artifacts around outliers. In practice, 20-40
histogram bins appear to work well, with more bins being required for skewed or
smaller datasets. Note that this function creates a histogram with non-uniform
bin widths. It offers no guarantees in terms of the mean-squared-error of the
histogram, but in practice is comparable to the histograms produced by the R/S-Plus
statistical computing packages. Note: the output type of the 'x' field in the return value is
propagated from the input value consumed in the aggregate function.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
nBins : :class:`~pyspark.sql.Column`
number of Histogram columns.
Returns
-------
:class:`~pyspark.sql.Column`
a histogram on numeric 'col' using nb bins.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.range(100, numPartitions=1)
>>> df.select(sf.histogram_numeric('id', sf.lit(5))).show(truncate=False)
+-----------------------------------------------------------+
|histogram_numeric(id, 5) |
+-----------------------------------------------------------+
|[{11, 25.0}, {36, 24.0}, {59, 23.0}, {84, 25.0}, {98, 3.0}]|
+-----------------------------------------------------------+
"""
return _invoke_function_over_columns("histogram_numeric", col, nBins)
@_try_remote_functions
def ntile(n: int) -> Column:
"""
Window function: returns the ntile group id (from 1 to `n` inclusive)
in an ordered window partition. For example, if `n` is 4, the first
quarter of the rows will get value 1, the second quarter will get 2,
the third quarter will get 3, and the last quarter will get 4.
This is equivalent to the NTILE function in SQL.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
n : int
an integer
Returns
-------
:class:`~pyspark.sql.Column`
portioned group id.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame(
... [("a", 1), ("a", 2), ("a", 3), ("b", 8), ("b", 2)], ["c1", "c2"])
>>> df.show()
+---+---+
| c1| c2|
+---+---+
| a| 1|
| a| 2|
| a| 3|
| b| 8|
| b| 2|
+---+---+
>>> w = Window.partitionBy("c1").orderBy("c2")
>>> df.withColumn("ntile", sf.ntile(2).over(w)).show()
+---+---+-----+
| c1| c2|ntile|
+---+---+-----+
| a| 1| 1|
| a| 2| 1|
| a| 3| 2|
| b| 2| 1|
| b| 8| 2|
+---+---+-----+
"""
return _invoke_function("ntile", int(_enum_to_value(n)))
# ---------------------- Date/Timestamp functions ------------------------------
@_try_remote_functions
def curdate() -> Column:
"""
Returns the current date at the start of query evaluation as a :class:`DateType` column.
All calls of current_date within the same query return the same value.
.. versionadded:: 3.5.0
Returns
-------
:class:`~pyspark.sql.Column`
current date.
See Also
--------
:meth:`pyspark.sql.functions.now`
:meth:`pyspark.sql.functions.current_date`
:meth:`pyspark.sql.functions.current_timestamp`
:meth:`pyspark.sql.functions.localtimestamp`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.curdate()).show() # doctest: +SKIP
+--------------+
|current_date()|
+--------------+
| 2022-08-26|
+--------------+
"""
return _invoke_function("curdate")
@_try_remote_functions
def current_date() -> Column:
"""
Returns the current date at the start of query evaluation as a :class:`DateType` column.
All calls of current_date within the same query return the same value.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`~pyspark.sql.Column`
current date.
See Also
--------
:meth:`pyspark.sql.functions.now`
:meth:`pyspark.sql.functions.curdate`
:meth:`pyspark.sql.functions.current_timestamp`
:meth:`pyspark.sql.functions.localtimestamp`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.current_date()).show() # doctest: +SKIP
+--------------+
|current_date()|
+--------------+
| 2022-08-26|
+--------------+
"""
return _invoke_function("current_date")
@_try_remote_functions
def current_timezone() -> Column:
"""
Returns the current session local timezone.
.. versionadded:: 3.5.0
Returns
-------
:class:`~pyspark.sql.Column`
current session local timezone.
See Also
--------
:meth:`pyspark.sql.functions.convert_timezone`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.current_timezone()).show()
+-------------------+
| current_timezone()|
+-------------------+
|America/Los_Angeles|
+-------------------+
Switch the timezone to Shanghai.
>>> spark.conf.set("spark.sql.session.timeZone", "Asia/Shanghai")
>>> spark.range(1).select(sf.current_timezone()).show()
+------------------+
|current_timezone()|
+------------------+
| Asia/Shanghai|
+------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
return _invoke_function("current_timezone")
@overload
def current_time() -> Column:
...
@overload
def current_time(precision: int) -> Column:
...
@_try_remote_functions
def current_time(precision: Optional[int] = None) -> Column:
"""
Returns the current time at the start of query evaluation as a :class:`TimeType` column. All
calls of current_time within the same query return the same value.
.. versionadded:: 4.1.0
Parameters
----------
precision: literal int, optional
number in the range [0..6], indicating how many fractional digits of seconds to include.
If omitted, the default is 6.
Returns
-------
:class:`~pyspark.sql.Column`
current time.
See Also
--------
:meth:`pyspark.sql.functions.current_date`
:meth:`pyspark.sql.functions.current_timestamp`
Examples
--------
Example 1: Current time with default precision
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.current_time().alias("time")).show() # doctest: +SKIP
+---------------+
| time|
+---------------+
|16:57:04.304361|
+---------------+
Example 2: Current time with specified precision
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.current_time(3).alias("time")).show() # doctest: +SKIP
+------------+
| time|
+------------+
|16:57:04.304|
+------------+
"""
if precision is None:
return _invoke_function("current_time")
else:
return _invoke_function("current_time", _enum_to_value(precision))
@_try_remote_functions
def current_timestamp() -> Column:
"""
Returns the current timestamp at the start of query evaluation as a :class:`TimestampType`
column. All calls of current_timestamp within the same query return the same value.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`~pyspark.sql.Column`
current date and time.
See Also
--------
:meth:`pyspark.sql.functions.now`
:meth:`pyspark.sql.functions.curdate`
:meth:`pyspark.sql.functions.current_date`
:meth:`pyspark.sql.functions.localtimestamp`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.current_timestamp()).show(truncate=False) # doctest: +SKIP
+-----------------------+
|current_timestamp() |
+-----------------------+
|2022-08-26 21:23:22.716|
+-----------------------+
"""
return _invoke_function("current_timestamp")
@_try_remote_functions
def now() -> Column:
"""
Returns the current timestamp at the start of query evaluation.
.. versionadded:: 3.5.0
Returns
-------
:class:`~pyspark.sql.Column`
current timestamp at the start of query evaluation.
See Also
--------
:meth:`pyspark.sql.functions.curdate`
:meth:`pyspark.sql.functions.current_date`
:meth:`pyspark.sql.functions.current_timestamp`
:meth:`pyspark.sql.functions.localtimestamp`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.now()).show(truncate=False) # doctest: +SKIP
+--------------------------+
|now() |
+--------------------------+
|2023-12-08 15:18:18.482269|
+--------------------------+
"""
return _invoke_function("now")
@_try_remote_functions
def localtimestamp() -> Column:
"""
Returns the current timestamp without time zone at the start of query evaluation
as a timestamp without time zone column. All calls of localtimestamp within the
same query return the same value.
.. versionadded:: 3.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`~pyspark.sql.Column`
current local date and time.
See Also
--------
:meth:`pyspark.sql.functions.now`
:meth:`pyspark.sql.functions.curdate`
:meth:`pyspark.sql.functions.current_date`
:meth:`pyspark.sql.functions.current_timestamp`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.localtimestamp()).show(truncate=False) # doctest: +SKIP
+-----------------------+
|localtimestamp() |
+-----------------------+
|2022-08-26 21:28:34.639|
+-----------------------+
"""
return _invoke_function("localtimestamp")
@_try_remote_functions
def date_format(date: "ColumnOrName", format: str) -> Column:
"""
Converts a date/timestamp/string to a value of string in the format specified by the date
format given by the second argument.
A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
pattern letters of `datetime pattern`_. can be used.
.. _datetime pattern: https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
Whenever possible, use specialized functions like `year`.
Parameters
----------
date : :class:`~pyspark.sql.Column` or column name
input column of values to format.
format: literal string
format to use to represent datetime values.
See Also
--------
:meth:`pyspark.sql.functions.to_date`
:meth:`pyspark.sql.functions.to_timestamp`
:meth:`pyspark.sql.functions.to_timestamp_ltz`
:meth:`pyspark.sql.functions.to_timestamp_ntz`
:meth:`pyspark.sql.functions.to_utc_timestamp`
:meth:`pyspark.sql.functions.try_to_timestamp`
Returns
-------
:class:`~pyspark.sql.Column`
string value representing formatted datetime.
Examples
--------
Example 1: Format a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.date_format('dt', 'MM/dd/yyyy')).show()
+----------+----------+---------------------------+
| dt|typeof(dt)|date_format(dt, MM/dd/yyyy)|
+----------+----------+---------------------------+
|2015-04-08| string| 04/08/2015|
|2024-10-31| string| 10/31/2024|
+----------+----------+---------------------------+
Example 2: Format a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.date_format('ts', 'yy=MM=dd HH=mm=ss')).show()
+-------------------+----------+----------------------------------+
| ts|typeof(ts)|date_format(ts, yy=MM=dd HH=mm=ss)|
+-------------------+----------+----------------------------------+
|2015-04-08 13:08:15| string| 15=04=08 13=08=15|
|2024-10-31 10:09:16| string| 24=10=31 10=09=16|
+-------------------+----------+----------------------------------+
Example 3: Format a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.date_format('dt', 'yy--MM--dd')).show()
+----------+----------+---------------------------+
| dt|typeof(dt)|date_format(dt, yy--MM--dd)|
+----------+----------+---------------------------+
|2015-04-08| date| 15--04--08|
|2024-10-31| date| 24--10--31|
+----------+----------+---------------------------+
Example 4: Format a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.date_format('ts', 'yy=MM=dd HH=mm=ss')).show()
+-------------------+----------+----------------------------------+
| ts|typeof(ts)|date_format(ts, yy=MM=dd HH=mm=ss)|
+-------------------+----------+----------------------------------+
|2015-04-08 13:08:15| timestamp| 15=04=08 13=08=15|
|2024-10-31 10:09:16| timestamp| 24=10=31 10=09=16|
+-------------------+----------+----------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("date_format", _to_java_column(date), _enum_to_value(format))
@_try_remote_functions
def year(col: "ColumnOrName") -> Column:
"""
Extract the year of a given date/timestamp as integer.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
year part of the date/timestamp as integer.
See Also
--------
:meth:`pyspark.sql.functions.quarter`
:meth:`pyspark.sql.functions.month`
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.hour`
:meth:`pyspark.sql.functions.minute`
:meth:`pyspark.sql.functions.second`
:meth:`pyspark.sql.functions.extract`
:meth:`pyspark.sql.functions.datepart`
:meth:`pyspark.sql.functions.date_part`
Examples
--------
Example 1: Extract the year from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.year('dt')).show()
+----------+----------+--------+
| dt|typeof(dt)|year(dt)|
+----------+----------+--------+
|2015-04-08| string| 2015|
|2024-10-31| string| 2024|
+----------+----------+--------+
Example 2: Extract the year from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.year('ts')).show()
+-------------------+----------+--------+
| ts|typeof(ts)|year(ts)|
+-------------------+----------+--------+
|2015-04-08 13:08:15| string| 2015|
|2024-10-31 10:09:16| string| 2024|
+-------------------+----------+--------+
Example 3: Extract the year from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.year('dt')).show()
+----------+----------+--------+
| dt|typeof(dt)|year(dt)|
+----------+----------+--------+
|2015-04-08| date| 2015|
|2024-10-31| date| 2024|
+----------+----------+--------+
Example 4: Extract the year from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.year('ts')).show()
+-------------------+----------+--------+
| ts|typeof(ts)|year(ts)|
+-------------------+----------+--------+
|2015-04-08 13:08:15| timestamp| 2015|
|2024-10-31 10:09:16| timestamp| 2024|
+-------------------+----------+--------+
"""
return _invoke_function_over_columns("year", col)
@_try_remote_functions
def quarter(col: "ColumnOrName") -> Column:
"""
Extract the quarter of a given date/timestamp as integer.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
quarter of the date/timestamp as integer.
See Also
--------
:meth:`pyspark.sql.functions.year`
:meth:`pyspark.sql.functions.month`
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.hour`
:meth:`pyspark.sql.functions.minute`
:meth:`pyspark.sql.functions.second`
:meth:`pyspark.sql.functions.extract`
:meth:`pyspark.sql.functions.datepart`
:meth:`pyspark.sql.functions.date_part`
Examples
--------
Example 1: Extract the quarter from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.quarter('dt')).show()
+----------+----------+-----------+
| dt|typeof(dt)|quarter(dt)|
+----------+----------+-----------+
|2015-04-08| string| 2|
|2024-10-31| string| 4|
+----------+----------+-----------+
Example 2: Extract the quarter from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.quarter('ts')).show()
+-------------------+----------+-----------+
| ts|typeof(ts)|quarter(ts)|
+-------------------+----------+-----------+
|2015-04-08 13:08:15| string| 2|
|2024-10-31 10:09:16| string| 4|
+-------------------+----------+-----------+
Example 3: Extract the quarter from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.quarter('dt')).show()
+----------+----------+-----------+
| dt|typeof(dt)|quarter(dt)|
+----------+----------+-----------+
|2015-04-08| date| 2|
|2024-10-31| date| 4|
+----------+----------+-----------+
Example 4: Extract the quarter from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.quarter('ts')).show()
+-------------------+----------+-----------+
| ts|typeof(ts)|quarter(ts)|
+-------------------+----------+-----------+
|2015-04-08 13:08:15| timestamp| 2|
|2024-10-31 10:09:16| timestamp| 4|
+-------------------+----------+-----------+
"""
return _invoke_function_over_columns("quarter", col)
@_try_remote_functions
def month(col: "ColumnOrName") -> Column:
"""
Extract the month of a given date/timestamp as integer.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
month part of the date/timestamp as integer.
See Also
--------
:meth:`pyspark.sql.functions.year`
:meth:`pyspark.sql.functions.quarter`
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.hour`
:meth:`pyspark.sql.functions.minute`
:meth:`pyspark.sql.functions.second`
:meth:`pyspark.sql.functions.monthname`
:meth:`pyspark.sql.functions.extract`
:meth:`pyspark.sql.functions.datepart`
:meth:`pyspark.sql.functions.date_part`
Examples
--------
Example 1: Extract the month from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.month('dt')).show()
+----------+----------+---------+
| dt|typeof(dt)|month(dt)|
+----------+----------+---------+
|2015-04-08| string| 4|
|2024-10-31| string| 10|
+----------+----------+---------+
Example 2: Extract the month from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.month('ts')).show()
+-------------------+----------+---------+
| ts|typeof(ts)|month(ts)|
+-------------------+----------+---------+
|2015-04-08 13:08:15| string| 4|
|2024-10-31 10:09:16| string| 10|
+-------------------+----------+---------+
Example 3: Extract the month from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.month('dt')).show()
+----------+----------+---------+
| dt|typeof(dt)|month(dt)|
+----------+----------+---------+
|2015-04-08| date| 4|
|2024-10-31| date| 10|
+----------+----------+---------+
Example 3: Extract the month from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.month('ts')).show()
+-------------------+----------+---------+
| ts|typeof(ts)|month(ts)|
+-------------------+----------+---------+
|2015-04-08 13:08:15| timestamp| 4|
|2024-10-31 10:09:16| timestamp| 10|
+-------------------+----------+---------+
"""
return _invoke_function_over_columns("month", col)
@_try_remote_functions
def dayofweek(col: "ColumnOrName") -> Column:
"""
Extract the day of the week of a given date/timestamp as integer.
Ranges from 1 for a Sunday through to 7 for a Saturday
.. versionadded:: 2.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
day of the week for given date/timestamp as integer.
See Also
--------
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.dayofyear`
:meth:`pyspark.sql.functions.dayofmonth`
:meth:`pyspark.sql.functions.weekofyear`
Examples
--------
Example 1: Extract the day of the week from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.dayofweek('dt')).show()
+----------+----------+-------------+
| dt|typeof(dt)|dayofweek(dt)|
+----------+----------+-------------+
|2015-04-08| string| 4|
|2024-10-31| string| 5|
+----------+----------+-------------+
Example 2: Extract the day of the week from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.dayofweek('ts')).show()
+-------------------+----------+-------------+
| ts|typeof(ts)|dayofweek(ts)|
+-------------------+----------+-------------+
|2015-04-08 13:08:15| string| 4|
|2024-10-31 10:09:16| string| 5|
+-------------------+----------+-------------+
Example 3: Extract the day of the week from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.dayofweek('dt')).show()
+----------+----------+-------------+
| dt|typeof(dt)|dayofweek(dt)|
+----------+----------+-------------+
|2015-04-08| date| 4|
|2024-10-31| date| 5|
+----------+----------+-------------+
Example 4: Extract the day of the week from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.dayofweek('ts')).show()
+-------------------+----------+-------------+
| ts|typeof(ts)|dayofweek(ts)|
+-------------------+----------+-------------+
|2015-04-08 13:08:15| timestamp| 4|
|2024-10-31 10:09:16| timestamp| 5|
+-------------------+----------+-------------+
"""
return _invoke_function_over_columns("dayofweek", col)
@_try_remote_functions
def dayofmonth(col: "ColumnOrName") -> Column:
"""
Extract the day of the month of a given date/timestamp as integer.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/timestamp column to work on.
See Also
--------
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.dayofyear`
:meth:`pyspark.sql.functions.dayofweek`
:meth:`pyspark.sql.functions.weekofyear`
Returns
-------
:class:`~pyspark.sql.Column`
day of the month for given date/timestamp as integer.
Examples
--------
Example 1: Extract the day of the month from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.dayofmonth('dt')).show()
+----------+----------+--------------+
| dt|typeof(dt)|dayofmonth(dt)|
+----------+----------+--------------+
|2015-04-08| string| 8|
|2024-10-31| string| 31|
+----------+----------+--------------+
Example 2: Extract the day of the month from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.dayofmonth('ts')).show()
+-------------------+----------+--------------+
| ts|typeof(ts)|dayofmonth(ts)|
+-------------------+----------+--------------+
|2015-04-08 13:08:15| string| 8|
|2024-10-31 10:09:16| string| 31|
+-------------------+----------+--------------+
Example 3: Extract the day of the month from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.dayofmonth('dt')).show()
+----------+----------+--------------+
| dt|typeof(dt)|dayofmonth(dt)|
+----------+----------+--------------+
|2015-04-08| date| 8|
|2024-10-31| date| 31|
+----------+----------+--------------+
Example 4: Extract the day of the month from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.dayofmonth('ts')).show()
+-------------------+----------+--------------+
| ts|typeof(ts)|dayofmonth(ts)|
+-------------------+----------+--------------+
|2015-04-08 13:08:15| timestamp| 8|
|2024-10-31 10:09:16| timestamp| 31|
+-------------------+----------+--------------+
"""
return _invoke_function_over_columns("dayofmonth", col)
@_try_remote_functions
def day(col: "ColumnOrName") -> Column:
"""
Extract the day of the month of a given date/timestamp as integer.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
day of the month for given date/timestamp as integer.
See Also
--------
:meth:`pyspark.sql.functions.year`
:meth:`pyspark.sql.functions.quarter`
:meth:`pyspark.sql.functions.month`
:meth:`pyspark.sql.functions.hour`
:meth:`pyspark.sql.functions.minute`
:meth:`pyspark.sql.functions.second`
:meth:`pyspark.sql.functions.dayname`
:meth:`pyspark.sql.functions.dayofyear`
:meth:`pyspark.sql.functions.dayofmonth`
:meth:`pyspark.sql.functions.dayofweek`
:meth:`pyspark.sql.functions.extract`
:meth:`pyspark.sql.functions.datepart`
:meth:`pyspark.sql.functions.date_part`
Examples
--------
Example 1: Extract the day of the month from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.day('dt')).show()
+----------+----------+-------+
| dt|typeof(dt)|day(dt)|
+----------+----------+-------+
|2015-04-08| string| 8|
|2024-10-31| string| 31|
+----------+----------+-------+
Example 2: Extract the day of the month from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.day('ts')).show()
+-------------------+----------+-------+
| ts|typeof(ts)|day(ts)|
+-------------------+----------+-------+
|2015-04-08 13:08:15| string| 8|
|2024-10-31 10:09:16| string| 31|
+-------------------+----------+-------+
Example 3: Extract the day of the month from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.day('dt')).show()
+----------+----------+-------+
| dt|typeof(dt)|day(dt)|
+----------+----------+-------+
|2015-04-08| date| 8|
|2024-10-31| date| 31|
+----------+----------+-------+
Example 4: Extract the day of the month from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.day('ts')).show()
+-------------------+----------+-------+
| ts|typeof(ts)|day(ts)|
+-------------------+----------+-------+
|2015-04-08 13:08:15| timestamp| 8|
|2024-10-31 10:09:16| timestamp| 31|
+-------------------+----------+-------+
"""
return _invoke_function_over_columns("day", col)
@_try_remote_functions
def dayofyear(col: "ColumnOrName") -> Column:
"""
Extract the day of the year of a given date/timestamp as integer.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
day of the year for given date/timestamp as integer.
See Also
--------
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.dayofyear`
:meth:`pyspark.sql.functions.dayofmonth`
:meth:`pyspark.sql.functions.weekofyear`
Examples
--------
Example 1: Extract the day of the year from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.dayofyear('dt')).show()
+----------+----------+-------------+
| dt|typeof(dt)|dayofyear(dt)|
+----------+----------+-------------+
|2015-04-08| string| 98|
|2024-10-31| string| 305|
+----------+----------+-------------+
Example 2: Extract the day of the year from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.dayofyear('ts')).show()
+-------------------+----------+-------------+
| ts|typeof(ts)|dayofyear(ts)|
+-------------------+----------+-------------+
|2015-04-08 13:08:15| string| 98|
|2024-10-31 10:09:16| string| 305|
+-------------------+----------+-------------+
Example 3: Extract the day of the year from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.dayofyear('dt')).show()
+----------+----------+-------------+
| dt|typeof(dt)|dayofyear(dt)|
+----------+----------+-------------+
|2015-04-08| date| 98|
|2024-10-31| date| 305|
+----------+----------+-------------+
Example 4: Extract the day of the year from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.dayofyear('ts')).show()
+-------------------+----------+-------------+
| ts|typeof(ts)|dayofyear(ts)|
+-------------------+----------+-------------+
|2015-04-08 13:08:15| timestamp| 98|
|2024-10-31 10:09:16| timestamp| 305|
+-------------------+----------+-------------+
"""
return _invoke_function_over_columns("dayofyear", col)
@_try_remote_functions
def hour(col: "ColumnOrName") -> Column:
"""
Extract the hours of a given timestamp as integer.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. versionchanged:: 4.1.0
Added support for time type.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/time/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
hour part of the timestamp as integer.
See Also
--------
:meth:`pyspark.sql.functions.year`
:meth:`pyspark.sql.functions.quarter`
:meth:`pyspark.sql.functions.month`
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.minute`
:meth:`pyspark.sql.functions.second`
:meth:`pyspark.sql.functions.extract`
:meth:`pyspark.sql.functions.datepart`
:meth:`pyspark.sql.functions.date_part`
Examples
--------
Example 1: Extract the hours from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.hour('ts')).show()
+-------------------+----------+--------+
| ts|typeof(ts)|hour(ts)|
+-------------------+----------+--------+
|2015-04-08 13:08:15| string| 13|
|2024-10-31 10:09:16| string| 10|
+-------------------+----------+--------+
Example 2: Extract the hours from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.hour('ts')).show()
+-------------------+----------+--------+
| ts|typeof(ts)|hour(ts)|
+-------------------+----------+--------+
|2015-04-08 13:08:15| timestamp| 13|
|2024-10-31 10:09:16| timestamp| 10|
+-------------------+----------+--------+
Example 3: Extract the hours from a time column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... ("13:08:15",),
... ("10:09:16",)], ['t']).withColumn("t", sf.col("t").cast("time"))
>>> df.select("*", sf.typeof('t'), sf.hour('t')).show()
+--------+---------+-------+
| t|typeof(t)|hour(t)|
+--------+---------+-------+
|13:08:15| time(6)| 13|
|10:09:16| time(6)| 10|
+--------+---------+-------+
"""
return _invoke_function_over_columns("hour", col)
@_try_remote_functions
def minute(col: "ColumnOrName") -> Column:
"""
Extract the minutes of a given timestamp as integer.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. versionchanged:: 4.1.0
Added support for time type.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/time/timestamp column to work on.
See Also
--------
:meth:`pyspark.sql.functions.year`
:meth:`pyspark.sql.functions.quarter`
:meth:`pyspark.sql.functions.month`
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.hour`
:meth:`pyspark.sql.functions.second`
:meth:`pyspark.sql.functions.extract`
:meth:`pyspark.sql.functions.datepart`
:meth:`pyspark.sql.functions.date_part`
Returns
-------
:class:`~pyspark.sql.Column`
minutes part of the timestamp as integer.
Examples
--------
Example 1: Extract the minutes from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.minute('ts')).show()
+-------------------+----------+----------+
| ts|typeof(ts)|minute(ts)|
+-------------------+----------+----------+
|2015-04-08 13:08:15| string| 8|
|2024-10-31 10:09:16| string| 9|
+-------------------+----------+----------+
Example 2: Extract the minutes from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.minute('ts')).show()
+-------------------+----------+----------+
| ts|typeof(ts)|minute(ts)|
+-------------------+----------+----------+
|2015-04-08 13:08:15| timestamp| 8|
|2024-10-31 10:09:16| timestamp| 9|
+-------------------+----------+----------+
Example 3: Extract the minutes from a time column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... ("13:08:15",),
... ("10:09:16",)], ['t']).withColumn("t", sf.col("t").cast("time"))
>>> df.select("*", sf.typeof('t'), sf.minute('t')).show()
+--------+---------+---------+
| t|typeof(t)|minute(t)|
+--------+---------+---------+
|13:08:15| time(6)| 8|
|10:09:16| time(6)| 9|
+--------+---------+---------+
"""
return _invoke_function_over_columns("minute", col)
@_try_remote_functions
def second(col: "ColumnOrName") -> Column:
"""
Extract the seconds of a given date as integer.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. versionchanged:: 4.1.0
Added support for time type.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/time/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
`seconds` part of the timestamp as integer.
See Also
--------
:meth:`pyspark.sql.functions.year`
:meth:`pyspark.sql.functions.quarter`
:meth:`pyspark.sql.functions.month`
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.hour`
:meth:`pyspark.sql.functions.minute`
:meth:`pyspark.sql.functions.extract`
:meth:`pyspark.sql.functions.datepart`
:meth:`pyspark.sql.functions.date_part`
Examples
--------
Example 1: Extract the seconds from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.second('ts')).show()
+-------------------+----------+----------+
| ts|typeof(ts)|second(ts)|
+-------------------+----------+----------+
|2015-04-08 13:08:15| string| 15|
|2024-10-31 10:09:16| string| 16|
+-------------------+----------+----------+
Example 2: Extract the seconds from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.second('ts')).show()
+-------------------+----------+----------+
| ts|typeof(ts)|second(ts)|
+-------------------+----------+----------+
|2015-04-08 13:08:15| timestamp| 15|
|2024-10-31 10:09:16| timestamp| 16|
+-------------------+----------+----------+
Example 3: Extract the seconds from a time column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... ("13:08:15",),
... ("10:09:16",)], ['t']).withColumn("t", sf.col("t").cast("time"))
>>> df.select("*", sf.typeof('t'), sf.second('t')).show()
+--------+---------+---------+
| t|typeof(t)|second(t)|
+--------+---------+---------+
|13:08:15| time(6)| 15|
|10:09:16| time(6)| 16|
+--------+---------+---------+
"""
return _invoke_function_over_columns("second", col)
@_try_remote_functions
def weekofyear(col: "ColumnOrName") -> Column:
"""
Extract the week number of a given date as integer.
A week is considered to start on a Monday and week 1 is the first week with more than 3 days,
as defined by ISO 8601
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
`week` of the year for given date as integer.
See Also
--------
:meth:`pyspark.sql.functions.weekday`
:meth:`pyspark.sql.functions.dayofweek`
:meth:`pyspark.sql.functions.dayofmonth`
:meth:`pyspark.sql.functions.dayofyear`
Examples
--------
Example 1: Extract the week of the year from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.weekofyear('dt')).show()
+----------+----------+--------------+
| dt|typeof(dt)|weekofyear(dt)|
+----------+----------+--------------+
|2015-04-08| string| 15|
|2024-10-31| string| 44|
+----------+----------+--------------+
Example 2: Extract the week of the year from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.weekofyear('ts')).show()
+-------------------+----------+--------------+
| ts|typeof(ts)|weekofyear(ts)|
+-------------------+----------+--------------+
|2015-04-08 13:08:15| string| 15|
|2024-10-31 10:09:16| string| 44|
+-------------------+----------+--------------+
Example 3: Extract the week of the year from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.weekofyear('dt')).show()
+----------+----------+--------------+
| dt|typeof(dt)|weekofyear(dt)|
+----------+----------+--------------+
|2015-04-08| date| 15|
|2024-10-31| date| 44|
+----------+----------+--------------+
Example 4: Extract the week of the year from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.weekofyear('ts')).show()
+-------------------+----------+--------------+
| ts|typeof(ts)|weekofyear(ts)|
+-------------------+----------+--------------+
|2015-04-08 13:08:15| timestamp| 15|
|2024-10-31 10:09:16| timestamp| 44|
+-------------------+----------+--------------+
"""
return _invoke_function_over_columns("weekofyear", col)
@_try_remote_functions
def weekday(col: "ColumnOrName") -> Column:
"""
Returns the day of the week for date/timestamp (0 = Monday, 1 = Tuesday, ..., 6 = Sunday).
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
the day of the week for date/timestamp (0 = Monday, 1 = Tuesday, ..., 6 = Sunday).
See Also
--------
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.weekofyear`
Examples
--------
Example 1: Extract the day of the week from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.weekday('dt')).show()
+----------+----------+-----------+
| dt|typeof(dt)|weekday(dt)|
+----------+----------+-----------+
|2015-04-08| string| 2|
|2024-10-31| string| 3|
+----------+----------+-----------+
Example 2: Extract the day of the week from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.weekday('ts')).show()
+-------------------+----------+-----------+
| ts|typeof(ts)|weekday(ts)|
+-------------------+----------+-----------+
|2015-04-08 13:08:15| string| 2|
|2024-10-31 10:09:16| string| 3|
+-------------------+----------+-----------+
Example 3: Extract the day of the week from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.weekday('dt')).show()
+----------+----------+-----------+
| dt|typeof(dt)|weekday(dt)|
+----------+----------+-----------+
|2015-04-08| date| 2|
|2024-10-31| date| 3|
+----------+----------+-----------+
Example 4: Extract the day of the week from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.weekday('ts')).show()
+-------------------+----------+-----------+
| ts|typeof(ts)|weekday(ts)|
+-------------------+----------+-----------+
|2015-04-08 13:08:15| timestamp| 2|
|2024-10-31 10:09:16| timestamp| 3|
+-------------------+----------+-----------+
"""
return _invoke_function_over_columns("weekday", col)
@_try_remote_functions
def monthname(col: "ColumnOrName") -> Column:
"""
Returns the three-letter abbreviated month name from the given date.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
the three-letter abbreviation of month name for date/timestamp (Jan, Feb, Mar...)
See Also
--------
:meth:`pyspark.sql.functions.month`
:meth:`pyspark.sql.functions.dayname`
Examples
--------
Example 1: Extract the month name from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.monthname('dt')).show()
+----------+----------+-------------+
| dt|typeof(dt)|monthname(dt)|
+----------+----------+-------------+
|2015-04-08| string| Apr|
|2024-10-31| string| Oct|
+----------+----------+-------------+
Example 2: Extract the month name from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.monthname('ts')).show()
+-------------------+----------+-------------+
| ts|typeof(ts)|monthname(ts)|
+-------------------+----------+-------------+
|2015-04-08 13:08:15| string| Apr|
|2024-10-31 10:09:16| string| Oct|
+-------------------+----------+-------------+
Example 3: Extract the month name from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.monthname('dt')).show()
+----------+----------+-------------+
| dt|typeof(dt)|monthname(dt)|
+----------+----------+-------------+
|2015-04-08| date| Apr|
|2024-10-31| date| Oct|
+----------+----------+-------------+
Example 4: Extract the month name from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.monthname('ts')).show()
+-------------------+----------+-------------+
| ts|typeof(ts)|monthname(ts)|
+-------------------+----------+-------------+
|2015-04-08 13:08:15| timestamp| Apr|
|2024-10-31 10:09:16| timestamp| Oct|
+-------------------+----------+-------------+
"""
return _invoke_function_over_columns("monthname", col)
@_try_remote_functions
def dayname(col: "ColumnOrName") -> Column:
"""
Date and Timestamp Function: Returns the three-letter abbreviated day name from the given date.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target date/timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
the three-letter abbreviation of day name for date/timestamp (Mon, Tue, Wed...)
See Also
--------
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.monthname`
Examples
--------
Example 1: Extract the weekday name from a string column representing dates
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08',), ('2024-10-31',)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.dayname('dt')).show()
+----------+----------+-----------+
| dt|typeof(dt)|dayname(dt)|
+----------+----------+-----------+
|2015-04-08| string| Wed|
|2024-10-31| string| Thu|
+----------+----------+-----------+
Example 2: Extract the weekday name from a string column representing timestamp
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08 13:08:15',), ('2024-10-31 10:09:16',)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.dayname('ts')).show()
+-------------------+----------+-----------+
| ts|typeof(ts)|dayname(ts)|
+-------------------+----------+-----------+
|2015-04-08 13:08:15| string| Wed|
|2024-10-31 10:09:16| string| Thu|
+-------------------+----------+-----------+
Example 3: Extract the weekday name from a date column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.date(2015, 4, 8),),
... (datetime.date(2024, 10, 31),)], ['dt'])
>>> df.select("*", sf.typeof('dt'), sf.dayname('dt')).show()
+----------+----------+-----------+
| dt|typeof(dt)|dayname(dt)|
+----------+----------+-----------+
|2015-04-08| date| Wed|
|2024-10-31| date| Thu|
+----------+----------+-----------+
Example 4: Extract the weekday name from a timestamp column
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (datetime.datetime(2015, 4, 8, 13, 8, 15),),
... (datetime.datetime(2024, 10, 31, 10, 9, 16),)], ['ts'])
>>> df.select("*", sf.typeof('ts'), sf.dayname('ts')).show()
+-------------------+----------+-----------+
| ts|typeof(ts)|dayname(ts)|
+-------------------+----------+-----------+
|2015-04-08 13:08:15| timestamp| Wed|
|2024-10-31 10:09:16| timestamp| Thu|
+-------------------+----------+-----------+
"""
return _invoke_function_over_columns("dayname", col)
@_try_remote_functions
def extract(field: Column, source: "ColumnOrName") -> Column:
"""
Extracts a part of the date/timestamp or interval source.
.. versionadded:: 3.5.0
Parameters
----------
field : :class:`~pyspark.sql.Column`
selects which part of the source should be extracted.
source : :class:`~pyspark.sql.Column` or column name
a date/timestamp or interval column from where `field` should be extracted.
Returns
-------
:class:`~pyspark.sql.Column`
a part of the date/timestamp or interval source.
See Also
--------
:meth:`pyspark.sql.functions.year`
:meth:`pyspark.sql.functions.quarter`
:meth:`pyspark.sql.functions.month`
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.hour`
:meth:`pyspark.sql.functions.minute`
:meth:`pyspark.sql.functions.second`
:meth:`pyspark.sql.functions.datepart`
:meth:`pyspark.sql.functions.date_part`
Examples
--------
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(datetime.datetime(2015, 4, 8, 13, 8, 15),)], ['ts'])
>>> df.select(
... '*',
... sf.extract(sf.lit('YEAR'), 'ts').alias('year'),
... sf.extract(sf.lit('month'), 'ts').alias('month'),
... sf.extract(sf.lit('WEEK'), 'ts').alias('week'),
... sf.extract(sf.lit('D'), df.ts).alias('day'),
... sf.extract(sf.lit('M'), df.ts).alias('minute'),
... sf.extract(sf.lit('S'), df.ts).alias('second')
... ).show()
+-------------------+----+-----+----+---+------+---------+
| ts|year|month|week|day|minute| second|
+-------------------+----+-----+----+---+------+---------+
|2015-04-08 13:08:15|2015| 4| 15| 8| 8|15.000000|
+-------------------+----+-----+----+---+------+---------+
"""
return _invoke_function_over_columns("extract", field, source)
@_try_remote_functions
def date_part(field: Column, source: "ColumnOrName") -> Column:
"""
Extracts a part of the date/timestamp or interval source.
.. versionadded:: 3.5.0
Parameters
----------
field : :class:`~pyspark.sql.Column`
selects which part of the source should be extracted, and supported string values
are as same as the fields of the equivalent function `extract`.
source : :class:`~pyspark.sql.Column` or column name
a date/timestamp or interval column from where `field` should be extracted.
Returns
-------
:class:`~pyspark.sql.Column`
a part of the date/timestamp or interval source.
See Also
--------
:meth:`pyspark.sql.functions.year`
:meth:`pyspark.sql.functions.quarter`
:meth:`pyspark.sql.functions.month`
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.hour`
:meth:`pyspark.sql.functions.minute`
:meth:`pyspark.sql.functions.second`
:meth:`pyspark.sql.functions.datepart`
:meth:`pyspark.sql.functions.extract`
Examples
--------
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(datetime.datetime(2015, 4, 8, 13, 8, 15),)], ['ts'])
>>> df.select(
... '*',
... sf.date_part(sf.lit('YEAR'), 'ts').alias('year'),
... sf.date_part(sf.lit('month'), 'ts').alias('month'),
... sf.date_part(sf.lit('WEEK'), 'ts').alias('week'),
... sf.date_part(sf.lit('D'), df.ts).alias('day'),
... sf.date_part(sf.lit('M'), df.ts).alias('minute'),
... sf.date_part(sf.lit('S'), df.ts).alias('second')
... ).show()
+-------------------+----+-----+----+---+------+---------+
| ts|year|month|week|day|minute| second|
+-------------------+----+-----+----+---+------+---------+
|2015-04-08 13:08:15|2015| 4| 15| 8| 8|15.000000|
+-------------------+----+-----+----+---+------+---------+
"""
return _invoke_function_over_columns("date_part", field, source)
@_try_remote_functions
def datepart(field: Column, source: "ColumnOrName") -> Column:
"""
Extracts a part of the date/timestamp or interval source.
.. versionadded:: 3.5.0
Parameters
----------
field : :class:`~pyspark.sql.Column`
selects which part of the source should be extracted, and supported string values
are as same as the fields of the equivalent function `extract`.
source : :class:`~pyspark.sql.Column` or column name
a date/timestamp or interval column from where `field` should be extracted.
Returns
-------
:class:`~pyspark.sql.Column`
a part of the date/timestamp or interval source.
See Also
--------
:meth:`pyspark.sql.functions.year`
:meth:`pyspark.sql.functions.quarter`
:meth:`pyspark.sql.functions.month`
:meth:`pyspark.sql.functions.day`
:meth:`pyspark.sql.functions.hour`
:meth:`pyspark.sql.functions.minute`
:meth:`pyspark.sql.functions.second`
:meth:`pyspark.sql.functions.date_part`
:meth:`pyspark.sql.functions.extract`
Examples
--------
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(datetime.datetime(2015, 4, 8, 13, 8, 15),)], ['ts'])
>>> df.select(
... '*',
... sf.datepart(sf.lit('YEAR'), 'ts').alias('year'),
... sf.datepart(sf.lit('month'), 'ts').alias('month'),
... sf.datepart(sf.lit('WEEK'), 'ts').alias('week'),
... sf.datepart(sf.lit('D'), df.ts).alias('day'),
... sf.datepart(sf.lit('M'), df.ts).alias('minute'),
... sf.datepart(sf.lit('S'), df.ts).alias('second')
... ).show()
+-------------------+----+-----+----+---+------+---------+
| ts|year|month|week|day|minute| second|
+-------------------+----+-----+----+---+------+---------+
|2015-04-08 13:08:15|2015| 4| 15| 8| 8|15.000000|
+-------------------+----+-----+----+---+------+---------+
"""
return _invoke_function_over_columns("datepart", field, source)
@_try_remote_functions
def make_date(year: "ColumnOrName", month: "ColumnOrName", day: "ColumnOrName") -> Column:
"""
Returns a column with a date built from the year, month and day columns.
.. versionadded:: 3.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
year : :class:`~pyspark.sql.Column` or column name
The year to build the date
month : :class:`~pyspark.sql.Column` or column name
The month to build the date
day : :class:`~pyspark.sql.Column` or column name
The day to build the date
Returns
-------
:class:`~pyspark.sql.Column`
a date built from given parts.
See Also
--------
:meth:`pyspark.sql.functions.make_timestamp`
:meth:`pyspark.sql.functions.make_timestamp_ltz`
:meth:`pyspark.sql.functions.make_timestamp_ntz`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(2020, 6, 26)], ['Y', 'M', 'D'])
>>> df.select('*', sf.make_date(df.Y, 'M', df.D)).show()
+----+---+---+------------------+
| Y| M| D|make_date(Y, M, D)|
+----+---+---+------------------+
|2020| 6| 26| 2020-06-26|
+----+---+---+------------------+
"""
return _invoke_function_over_columns("make_date", year, month, day)
@_try_remote_functions
def date_add(start: "ColumnOrName", days: Union["ColumnOrName", int]) -> Column:
"""
Returns the date that is `days` days after `start`. If `days` is a negative value
then these amount of days will be deducted from `start`.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
start : :class:`~pyspark.sql.Column` or column name
date column to work on.
days : :class:`~pyspark.sql.Column` or column name or int
how many days after the given date to calculate.
Accepts negative value as well to calculate backwards in time.
Returns
-------
:class:`~pyspark.sql.Column`
a date after/before given number of days.
See Also
--------
:meth:`pyspark.sql.functions.dateadd`
:meth:`pyspark.sql.functions.date_sub`
:meth:`pyspark.sql.functions.datediff`
:meth:`pyspark.sql.functions.date_diff`
:meth:`pyspark.sql.functions.timestamp_add`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-04-08', 2,)], 'struct<dt:string,a:int>')
>>> df.select('*', sf.date_add(df.dt, 1)).show()
+----------+---+---------------+
| dt| a|date_add(dt, 1)|
+----------+---+---------------+
|2015-04-08| 2| 2015-04-09|
+----------+---+---------------+
>>> df.select('*', sf.date_add('dt', 'a')).show()
+----------+---+---------------+
| dt| a|date_add(dt, a)|
+----------+---+---------------+
|2015-04-08| 2| 2015-04-10|
+----------+---+---------------+
>>> df.select('*', sf.date_add('dt', sf.lit(-1))).show()
+----------+---+----------------+
| dt| a|date_add(dt, -1)|
+----------+---+----------------+
|2015-04-08| 2| 2015-04-07|
+----------+---+----------------+
"""
days = _enum_to_value(days)
days = lit(days) if isinstance(days, int) else days
return _invoke_function_over_columns("date_add", start, days)
@_try_remote_functions
def dateadd(start: "ColumnOrName", days: Union["ColumnOrName", int]) -> Column:
"""
Returns the date that is `days` days after `start`. If `days` is a negative value
then these amount of days will be deducted from `start`.
.. versionadded:: 3.5.0
Parameters
----------
start : :class:`~pyspark.sql.Column` or column name
date column to work on.
days : :class:`~pyspark.sql.Column` or column name or int
how many days after the given date to calculate.
Accepts negative value as well to calculate backwards in time.
Returns
-------
:class:`~pyspark.sql.Column`
a date after/before given number of days.
See Also
--------
:meth:`pyspark.sql.functions.date_add`
:meth:`pyspark.sql.functions.date_sub`
:meth:`pyspark.sql.functions.datediff`
:meth:`pyspark.sql.functions.date_diff`
:meth:`pyspark.sql.functions.timestamp_add`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08', 2,)], 'struct<dt:string,a:int>')
>>> df.select('*', sf.dateadd(df.dt, 1)).show()
+----------+---+---------------+
| dt| a|date_add(dt, 1)|
+----------+---+---------------+
|2015-04-08| 2| 2015-04-09|
+----------+---+---------------+
>>> df.select('*', sf.dateadd('dt', 'a')).show()
+----------+---+---------------+
| dt| a|date_add(dt, a)|
+----------+---+---------------+
|2015-04-08| 2| 2015-04-10|
+----------+---+---------------+
>>> df.select('*', sf.dateadd('dt', sf.lit(-1))).show()
+----------+---+----------------+
| dt| a|date_add(dt, -1)|
+----------+---+----------------+
|2015-04-08| 2| 2015-04-07|
+----------+---+----------------+
"""
days = _enum_to_value(days)
days = lit(days) if isinstance(days, int) else days
return _invoke_function_over_columns("dateadd", start, days)
@_try_remote_functions
def date_sub(start: "ColumnOrName", days: Union["ColumnOrName", int]) -> Column:
"""
Returns the date that is `days` days before `start`. If `days` is a negative value
then these amount of days will be added to `start`.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
start : :class:`~pyspark.sql.Column` or column name
date column to work on.
days : :class:`~pyspark.sql.Column` or column name or int
how many days before the given date to calculate.
Accepts negative value as well to calculate forward in time.
Returns
-------
:class:`~pyspark.sql.Column`
a date before/after given number of days.
See Also
--------
:meth:`pyspark.sql.functions.dateadd`
:meth:`pyspark.sql.functions.date_add`
:meth:`pyspark.sql.functions.datediff`
:meth:`pyspark.sql.functions.date_diff`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08', 2,)], 'struct<dt:string,a:int>')
>>> df.select('*', sf.date_sub(df.dt, 1)).show()
+----------+---+---------------+
| dt| a|date_sub(dt, 1)|
+----------+---+---------------+
|2015-04-08| 2| 2015-04-07|
+----------+---+---------------+
>>> df.select('*', sf.date_sub('dt', 'a')).show()
+----------+---+---------------+
| dt| a|date_sub(dt, a)|
+----------+---+---------------+
|2015-04-08| 2| 2015-04-06|
+----------+---+---------------+
>>> df.select('*', sf.date_sub('dt', sf.lit(-1))).show()
+----------+---+----------------+
| dt| a|date_sub(dt, -1)|
+----------+---+----------------+
|2015-04-08| 2| 2015-04-09|
+----------+---+----------------+
"""
days = _enum_to_value(days)
days = lit(days) if isinstance(days, int) else days
return _invoke_function_over_columns("date_sub", start, days)
@_try_remote_functions
def datediff(end: "ColumnOrName", start: "ColumnOrName") -> Column:
"""
Returns the number of days from `start` to `end`.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
end : :class:`~pyspark.sql.Column` or column name
to date column to work on.
start : :class:`~pyspark.sql.Column` or column name
from date column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
difference in days between two dates.
See Also
--------
:meth:`pyspark.sql.functions.dateadd`
:meth:`pyspark.sql.functions.date_add`
:meth:`pyspark.sql.functions.date_sub`
:meth:`pyspark.sql.functions.date_diff`
:meth:`pyspark.sql.functions.timestamp_diff`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08','2015-05-10')], ['d1', 'd2'])
>>> df.select('*', sf.datediff('d1', 'd2')).show()
+----------+----------+----------------+
| d1| d2|datediff(d1, d2)|
+----------+----------+----------------+
|2015-04-08|2015-05-10| -32|
+----------+----------+----------------+
>>> df.select('*', sf.datediff(df.d2, df.d1)).show()
+----------+----------+----------------+
| d1| d2|datediff(d2, d1)|
+----------+----------+----------------+
|2015-04-08|2015-05-10| 32|
+----------+----------+----------------+
"""
return _invoke_function_over_columns("datediff", end, start)
@_try_remote_functions
def date_diff(end: "ColumnOrName", start: "ColumnOrName") -> Column:
"""
Returns the number of days from `start` to `end`.
.. versionadded:: 3.5.0
Parameters
----------
end : :class:`~pyspark.sql.Column` or column name
to date column to work on.
start : :class:`~pyspark.sql.Column` or column name
from date column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
difference in days between two dates.
See Also
--------
:meth:`pyspark.sql.functions.dateadd`
:meth:`pyspark.sql.functions.date_add`
:meth:`pyspark.sql.functions.date_sub`
:meth:`pyspark.sql.functions.datediff`
:meth:`pyspark.sql.functions.timestamp_diff`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08','2015-05-10')], ['d1', 'd2'])
>>> df.select('*', sf.date_diff('d1', 'd2')).show()
+----------+----------+-----------------+
| d1| d2|date_diff(d1, d2)|
+----------+----------+-----------------+
|2015-04-08|2015-05-10| -32|
+----------+----------+-----------------+
>>> df.select('*', sf.date_diff(df.d2, df.d1)).show()
+----------+----------+-----------------+
| d1| d2|date_diff(d2, d1)|
+----------+----------+-----------------+
|2015-04-08|2015-05-10| 32|
+----------+----------+-----------------+
"""
return _invoke_function_over_columns("date_diff", end, start)
@_try_remote_functions
def date_from_unix_date(days: "ColumnOrName") -> Column:
"""
Create date from the number of `days` since 1970-01-01.
.. versionadded:: 3.5.0
Parameters
----------
days : :class:`~pyspark.sql.Column` or column name
the target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
the date from the number of days since 1970-01-01.
See Also
--------
:meth:`pyspark.sql.functions.from_unixtime`
:meth:`pyspark.sql.functions.unix_date`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(4).select('*', sf.date_from_unix_date('id')).show()
+---+-----------------------+
| id|date_from_unix_date(id)|
+---+-----------------------+
| 0| 1970-01-01|
| 1| 1970-01-02|
| 2| 1970-01-03|
| 3| 1970-01-04|
+---+-----------------------+
"""
return _invoke_function_over_columns("date_from_unix_date", days)
@_try_remote_functions
def add_months(start: "ColumnOrName", months: Union["ColumnOrName", int]) -> Column:
"""
Returns the date that is `months` months after `start`. If `months` is a negative value
then these amount of months will be deducted from the `start`.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
start : :class:`~pyspark.sql.Column` or column name
date column to work on.
months : :class:`~pyspark.sql.Column` or column name or int
how many months after the given date to calculate.
Accepts negative value as well to calculate backwards.
Returns
-------
:class:`~pyspark.sql.Column`
a date after/before given number of months.
See Also
--------
:meth:`pyspark.sql.functions.dateadd`
:meth:`pyspark.sql.functions.date_add`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08', 2,)], 'struct<dt:string,a:int>')
>>> df.select('*', sf.add_months(df.dt, 1)).show()
+----------+---+-----------------+
| dt| a|add_months(dt, 1)|
+----------+---+-----------------+
|2015-04-08| 2| 2015-05-08|
+----------+---+-----------------+
>>> df.select('*', sf.add_months('dt', 'a')).show()
+----------+---+-----------------+
| dt| a|add_months(dt, a)|
+----------+---+-----------------+
|2015-04-08| 2| 2015-06-08|
+----------+---+-----------------+
>>> df.select('*', sf.add_months('dt', sf.lit(-1))).show()
+----------+---+------------------+
| dt| a|add_months(dt, -1)|
+----------+---+------------------+
|2015-04-08| 2| 2015-03-08|
+----------+---+------------------+
"""
months = _enum_to_value(months)
months = lit(months) if isinstance(months, int) else months
return _invoke_function_over_columns("add_months", start, months)
@_try_remote_functions
def months_between(date1: "ColumnOrName", date2: "ColumnOrName", roundOff: bool = True) -> Column:
"""
Returns number of months between dates date1 and date2.
If date1 is later than date2, then the result is positive.
A whole number is returned if both inputs have the same day of month or both are the last day
of their respective months. Otherwise, the difference is calculated assuming 31 days per month.
The result is rounded off to 8 digits unless `roundOff` is set to `False`.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
date1 : :class:`~pyspark.sql.Column` or column name
first date column.
date2 : :class:`~pyspark.sql.Column` or column name
second date column.
roundOff : bool, optional
whether to round (to 8 digits) the final value or not (default: True).
Returns
-------
:class:`~pyspark.sql.Column`
number of months between two dates.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28 10:30:00', '1996-10-30')], ['d1', 'd2'])
>>> df.select('*', sf.months_between(df.d1, df.d2)).show()
+-------------------+----------+----------------------------+
| d1| d2|months_between(d1, d2, true)|
+-------------------+----------+----------------------------+
|1997-02-28 10:30:00|1996-10-30| 3.94959677|
+-------------------+----------+----------------------------+
>>> df.select('*', sf.months_between('d2', 'd1')).show()
+-------------------+----------+----------------------------+
| d1| d2|months_between(d2, d1, true)|
+-------------------+----------+----------------------------+
|1997-02-28 10:30:00|1996-10-30| -3.94959677|
+-------------------+----------+----------------------------+
>>> df.select('*', sf.months_between('d1', df.d2, False)).show()
+-------------------+----------+-----------------------------+
| d1| d2|months_between(d1, d2, false)|
+-------------------+----------+-----------------------------+
|1997-02-28 10:30:00|1996-10-30| 3.9495967741935...|
+-------------------+----------+-----------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"months_between", _to_java_column(date1), _to_java_column(date2), _enum_to_value(roundOff)
)
@_try_remote_functions
def to_date(col: "ColumnOrName", format: Optional[str] = None) -> Column:
"""Converts a :class:`~pyspark.sql.Column` into :class:`pyspark.sql.types.DateType`
using the optionally specified format. Specify formats according to `datetime pattern`_.
By default, it follows casting rules to :class:`pyspark.sql.types.DateType` if the format
is omitted. Equivalent to ``col.cast("date")``.
.. _datetime pattern: https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
.. versionadded:: 2.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column of values to convert.
format: literal string, optional
format to use to convert date values.
Returns
-------
:class:`~pyspark.sql.Column`
date value as :class:`pyspark.sql.types.DateType` type.
See Also
--------
:meth:`pyspark.sql.functions.to_timestamp`
:meth:`pyspark.sql.functions.to_timestamp_ltz`
:meth:`pyspark.sql.functions.to_timestamp_ntz`
:meth:`pyspark.sql.functions.to_utc_timestamp`
:meth:`pyspark.sql.functions.try_to_timestamp`
:meth:`pyspark.sql.functions.date_format`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['ts'])
>>> df.select('*', sf.to_date(df.ts)).show()
+-------------------+-----------+
| ts|to_date(ts)|
+-------------------+-----------+
|1997-02-28 10:30:00| 1997-02-28|
+-------------------+-----------+
>>> df.select('*', sf.to_date('ts', 'yyyy-MM-dd HH:mm:ss')).show()
+-------------------+--------------------------------+
| ts|to_date(ts, yyyy-MM-dd HH:mm:ss)|
+-------------------+--------------------------------+
|1997-02-28 10:30:00| 1997-02-28|
+-------------------+--------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
if format is None:
return _invoke_function_over_columns("to_date", col)
else:
return _invoke_function("to_date", _to_java_column(col), _enum_to_value(format))
@_try_remote_functions
def try_to_date(col: "ColumnOrName", format: Optional[str] = None) -> Column:
"""This is a special version of `try_to_date` that performs the same operation, but returns a
NULL value instead of raising an error if date cannot be created.
.. _datetime pattern: https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column of values to convert.
format: literal string, optional
format to use to convert date values.
Returns
-------
:class:`~pyspark.sql.Column`
date value as :class:`pyspark.sql.types.DateType` type.
See Also
--------
:meth:`pyspark.sql.functions.to_timestamp`
:meth:`pyspark.sql.functions.to_timestamp_ltz`
:meth:`pyspark.sql.functions.to_timestamp_ntz`
:meth:`pyspark.sql.functions.to_utc_timestamp`
:meth:`pyspark.sql.functions.try_to_timestamp`
:meth:`pyspark.sql.functions.date_format`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28',)], ['ts'])
>>> df.select('*', sf.try_to_date(df.ts)).show()
+----------+---------------+
| ts|try_to_date(ts)|
+----------+---------------+
|1997-02-28| 1997-02-28|
+----------+---------------+
>>> df.select('*', sf.try_to_date('ts', 'yyyy-MM-dd')).show()
+----------+---------------------------+
| ts|try_to_date(ts, yyyy-MM-dd)|
+----------+---------------------------+
|1997-02-28| 1997-02-28|
+----------+---------------------------+
>>> df = spark.createDataFrame([('foo',)], ['ts'])
>>> df.select(sf.try_to_date(df.ts)).show()
+---------------+
|try_to_date(ts)|
+---------------+
| NULL|
+---------------+
"""
from pyspark.sql.classic.column import _to_java_column
if format is None:
return _invoke_function_over_columns("try_to_date", col)
else:
return _invoke_function("try_to_date", _to_java_column(col), _enum_to_value(format))
@_try_remote_functions
def unix_date(col: "ColumnOrName") -> Column:
"""Returns the number of days since 1970-01-01.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column of values to convert.
Returns
-------
:class:`~pyspark.sql.Column`
the number of days since 1970-01-01.
See Also
--------
:meth:`pyspark.sql.functions.date_from_unix_date`
:meth:`pyspark.sql.functions.unix_seconds`
:meth:`pyspark.sql.functions.unix_millis`
:meth:`pyspark.sql.functions.unix_micros`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1970-01-02',), ('2022-01-02',)], ['dt'])
>>> df.select('*', sf.unix_date(sf.to_date('dt'))).show()
+----------+----------------------+
| dt|unix_date(to_date(dt))|
+----------+----------------------+
|1970-01-02| 1|
|2022-01-02| 18994|
+----------+----------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
return _invoke_function_over_columns("unix_date", col)
@_try_remote_functions
def unix_micros(col: "ColumnOrName") -> Column:
"""Returns the number of microseconds since 1970-01-01 00:00:00 UTC.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column of values to convert.
Returns
-------
:class:`~pyspark.sql.Column`
the number of microseconds since 1970-01-01 00:00:00 UTC.
See Also
--------
:meth:`pyspark.sql.functions.unix_date`
:meth:`pyspark.sql.functions.unix_seconds`
:meth:`pyspark.sql.functions.unix_millis`
:meth:`pyspark.sql.functions.timestamp_micros`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-07-22 10:00:00',), ('2022-10-09 11:12:13',)], ['ts'])
>>> df.select('*', sf.unix_micros(sf.to_timestamp('ts'))).show()
+-------------------+-----------------------------+
| ts|unix_micros(to_timestamp(ts))|
+-------------------+-----------------------------+
|2015-07-22 10:00:00| 1437584400000000|
|2022-10-09 11:12:13| 1665339133000000|
+-------------------+-----------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
return _invoke_function_over_columns("unix_micros", col)
@_try_remote_functions
def unix_millis(col: "ColumnOrName") -> Column:
"""Returns the number of milliseconds since 1970-01-01 00:00:00 UTC.
Truncates higher levels of precision.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column of values to convert.
Returns
-------
:class:`~pyspark.sql.Column`
the number of milliseconds since 1970-01-01 00:00:00 UTC.
See Also
--------
:meth:`pyspark.sql.functions.unix_date`
:meth:`pyspark.sql.functions.unix_seconds`
:meth:`pyspark.sql.functions.unix_micros`
:meth:`pyspark.sql.functions.timestamp_millis`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-07-22 10:00:00',), ('2022-10-09 11:12:13',)], ['ts'])
>>> df.select('*', sf.unix_millis(sf.to_timestamp('ts'))).show()
+-------------------+-----------------------------+
| ts|unix_millis(to_timestamp(ts))|
+-------------------+-----------------------------+
|2015-07-22 10:00:00| 1437584400000|
|2022-10-09 11:12:13| 1665339133000|
+-------------------+-----------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
return _invoke_function_over_columns("unix_millis", col)
@_try_remote_functions
def unix_seconds(col: "ColumnOrName") -> Column:
"""Returns the number of seconds since 1970-01-01 00:00:00 UTC.
Truncates higher levels of precision.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column of values to convert.
Returns
-------
:class:`~pyspark.sql.Column`
the number of seconds since 1970-01-01 00:00:00 UTC.
See Also
--------
:meth:`pyspark.sql.functions.unix_date`
:meth:`pyspark.sql.functions.unix_millis`
:meth:`pyspark.sql.functions.unix_micros`
:meth:`pyspark.sql.functions.from_unixtime`
:meth:`pyspark.sql.functions.timestamp_seconds`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-07-22 10:00:00',), ('2022-10-09 11:12:13',)], ['ts'])
>>> df.select('*', sf.unix_seconds(sf.to_timestamp('ts'))).show()
+-------------------+------------------------------+
| ts|unix_seconds(to_timestamp(ts))|
+-------------------+------------------------------+
|2015-07-22 10:00:00| 1437584400|
|2022-10-09 11:12:13| 1665339133|
+-------------------+------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
return _invoke_function_over_columns("unix_seconds", col)
@overload
def to_time(str: "ColumnOrName") -> Column:
...
@overload
def to_time(str: "ColumnOrName", format: "ColumnOrName") -> Column:
...
@_try_remote_functions
def to_time(str: "ColumnOrName", format: Optional["ColumnOrName"] = None) -> Column:
"""Converts a :class:`~pyspark.sql.Column` into :class:`pyspark.sql.types.TimeType` using the
optionally specified format. Specify formats according to `datetime pattern`_. By default, it
follows casting rules to :class:`pyspark.sql.types.TimeType` if the format is omitted.
Equivalent to ``col.cast("time")``.
.. _datetime pattern: https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
.. versionadded:: 4.1.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
string to be parsed to time.
format: :class:`~pyspark.sql.Column` or column name, optional
time format pattern to follow.
Returns
-------
:class:`~pyspark.sql.Column`
time value as :class:`pyspark.sql.types.TimeType` type.
See Also
--------
:meth:`pyspark.sql.functions.to_timestamp`
:meth:`pyspark.sql.functions.try_to_time`
Examples
--------
Example 1: Convert string to a time
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("10:30:00",)], ["str"])
>>> df.select(sf.to_time(df.str)).show()
+------------+
|to_time(str)|
+------------+
| 10:30:00|
+------------+
Example 2: Convert string to a time with a format
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("10:30:00", "HH:mm:ss")], ["str", "format"])
>>> df.select(sf.to_time(df.str, df.format)).show()
+--------------------+
|to_time(str, format)|
+--------------------+
| 10:30:00|
+--------------------+
"""
if format is None:
return _invoke_function_over_columns("to_time", str)
else:
return _invoke_function_over_columns("to_time", str, format)
@overload
def to_timestamp(col: "ColumnOrName") -> Column:
...
@overload
def to_timestamp(col: "ColumnOrName", format: str) -> Column:
...
@_try_remote_functions
def to_timestamp(col: "ColumnOrName", format: Optional[str] = None) -> Column:
"""Converts a :class:`~pyspark.sql.Column` into :class:`pyspark.sql.types.TimestampType`
using the optionally specified format. Specify formats according to `datetime pattern`_.
By default, it follows casting rules to :class:`pyspark.sql.types.TimestampType` if the format
is omitted. Equivalent to ``col.cast("timestamp")``.
.. _datetime pattern: https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
.. versionadded:: 2.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column values to convert.
format: literal string, optional
format to use to convert timestamp values.
Returns
-------
:class:`~pyspark.sql.Column`
timestamp value as :class:`pyspark.sql.types.TimestampType` type.
See Also
--------
:meth:`pyspark.sql.functions.to_date`
:meth:`pyspark.sql.functions.to_timestamp_ltz`
:meth:`pyspark.sql.functions.to_timestamp_ntz`
:meth:`pyspark.sql.functions.to_utc_timestamp`
:meth:`pyspark.sql.functions.to_unix_timestamp`
:meth:`pyspark.sql.functions.try_to_timestamp`
:meth:`pyspark.sql.functions.date_format`
Examples
--------
Example 1: Convert string to a timestamp
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(sf.to_timestamp(df.t)).show()
+-------------------+
| to_timestamp(t)|
+-------------------+
|1997-02-28 10:30:00|
+-------------------+
Example 2: Convert string to a timestamp with a format
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(sf.to_timestamp(df.t, 'yyyy-MM-dd HH:mm:ss')).show()
+------------------------------------+
|to_timestamp(t, yyyy-MM-dd HH:mm:ss)|
+------------------------------------+
| 1997-02-28 10:30:00|
+------------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
if format is None:
return _invoke_function_over_columns("to_timestamp", col)
else:
return _invoke_function("to_timestamp", _to_java_column(col), _enum_to_value(format))
@overload
def try_to_time(str: "ColumnOrName") -> Column:
...
@overload
def try_to_time(str: "ColumnOrName", format: "ColumnOrName") -> Column:
...
@_try_remote_functions
def try_to_time(str: "ColumnOrName", format: Optional["ColumnOrName"] = None) -> Column:
"""Converts a :class:`~pyspark.sql.Column` into :class:`pyspark.sql.types.TimeType` using the
optionally specified format. Specify formats according to `datetime pattern`_. By default, it
follows casting rules to :class:`pyspark.sql.types.TimeType` if the format is omitted.
Equivalent to ``col.cast("time")``. The function always returns null on an invalid input.
.. _datetime pattern: https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
.. versionadded:: 4.1.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
string to be parsed to time.
format: :class:`~pyspark.sql.Column` or column name, optional
time format pattern to follow.
Returns
-------
:class:`~pyspark.sql.Column`
time value as :class:`pyspark.sql.types.TimeType` type.
See Also
--------
:meth:`pyspark.sql.functions.to_time`
:meth:`pyspark.sql.functions.try_to_timestamp`
Examples
--------
Example 1: Convert string to a time
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("10:30:00",)], ["str"])
>>> df.select(sf.try_to_time(df.str).alias("time")).show()
+--------+
| time|
+--------+
|10:30:00|
+--------+
Example 2: Convert string to a time with a format
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("10:30:00", "HH:mm:ss")], ["str", "format"])
>>> df.select(sf.try_to_time(df.str, df.format).alias("time")).show()
+--------+
| time|
+--------+
|10:30:00|
+--------+
Example 3: Converion failure results in NULL
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("malformed",)], ["str"])
>>> df.select(sf.try_to_time(df.str).alias("time")).show()
+----+
|time|
+----+
|NULL|
+----+
"""
if format is None:
return _invoke_function_over_columns("try_to_time", str)
else:
return _invoke_function_over_columns("try_to_time", str, format)
@_try_remote_functions
def try_to_timestamp(col: "ColumnOrName", format: Optional["ColumnOrName"] = None) -> Column:
"""
Parses the `col` with the `format` to a timestamp. The function always
returns null on an invalid input with/without ANSI SQL mode enabled. The result data type is
consistent with the value of configuration `spark.sql.timestampType`.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column values to convert.
format: literal string, optional
format to use to convert timestamp values.
See Also
--------
:meth:`pyspark.sql.functions.to_date`
:meth:`pyspark.sql.functions.to_timestamp`
:meth:`pyspark.sql.functions.to_utc_timestamp`
:meth:`pyspark.sql.functions.date_format`
Examples
--------
Example 1: Convert string to a timestamp
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(sf.try_to_timestamp(df.t)).show()
+-------------------+
|try_to_timestamp(t)|
+-------------------+
|1997-02-28 10:30:00|
+-------------------+
Example 2: Convert string to a timestamp with a format
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(sf.try_to_timestamp(df.t, sf.lit('yyyy-MM-dd HH:mm:ss'))).show()
+----------------------------------------+
|try_to_timestamp(t, yyyy-MM-dd HH:mm:ss)|
+----------------------------------------+
| 1997-02-28 10:30:00|
+----------------------------------------+
Example 3: Converion failure results in NULL when ANSI mode is on
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
... df = spark.createDataFrame([('malformed',)], ['t'])
... df.select(sf.try_to_timestamp(df.t)).show()
... finally:
... spark.conf.set("spark.sql.ansi.enabled", origin)
+-------------------+
|try_to_timestamp(t)|
+-------------------+
| NULL|
+-------------------+
"""
if format is not None:
return _invoke_function_over_columns("try_to_timestamp", col, format)
else:
return _invoke_function_over_columns("try_to_timestamp", col)
@_try_remote_functions
def xpath(xml: "ColumnOrName", path: "ColumnOrName") -> Column:
"""
Returns a string array of values within the nodes of xml that match the XPath expression.
.. versionadded:: 3.5.0
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [('<a><b>b1</b><b>b2</b><b>b3</b><c>c1</c><c>c2</c></a>',)], ['x'])
>>> df.select(sf.xpath(df.x, sf.lit('a/b/text()'))).show()
+--------------------+
|xpath(x, a/b/text())|
+--------------------+
| [b1, b2, b3]|
+--------------------+
"""
return _invoke_function_over_columns("xpath", xml, path)
@_try_remote_functions
def xpath_boolean(xml: "ColumnOrName", path: "ColumnOrName") -> Column:
"""
Returns true if the XPath expression evaluates to true, or if a matching node is found.
.. versionadded:: 3.5.0
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('<a><b>1</b></a>',)], ['x'])
>>> df.select(sf.xpath_boolean(df.x, sf.lit('a/b'))).show()
+---------------------+
|xpath_boolean(x, a/b)|
+---------------------+
| true|
+---------------------+
"""
return _invoke_function_over_columns("xpath_boolean", xml, path)
@_try_remote_functions
def xpath_double(xml: "ColumnOrName", path: "ColumnOrName") -> Column:
"""
Returns a double value, the value zero if no match is found,
or NaN if a match is found but the value is non-numeric.
.. versionadded:: 3.5.0
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('<a><b>1</b><b>2</b></a>',)], ['x'])
>>> df.select(sf.xpath_double(df.x, sf.lit('sum(a/b)'))).show()
+-------------------------+
|xpath_double(x, sum(a/b))|
+-------------------------+
| 3.0|
+-------------------------+
"""
return _invoke_function_over_columns("xpath_double", xml, path)
@_try_remote_functions
def xpath_number(xml: "ColumnOrName", path: "ColumnOrName") -> Column:
"""
Returns a double value, the value zero if no match is found,
or NaN if a match is found but the value is non-numeric.
.. versionadded:: 3.5.0
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [('<a><b>1</b><b>2</b></a>',)], ['x']
... ).select(sf.xpath_number('x', sf.lit('sum(a/b)'))).show()
+-------------------------+
|xpath_number(x, sum(a/b))|
+-------------------------+
| 3.0|
+-------------------------+
"""
return _invoke_function_over_columns("xpath_number", xml, path)
@_try_remote_functions
def xpath_float(xml: "ColumnOrName", path: "ColumnOrName") -> Column:
"""
Returns a float value, the value zero if no match is found,
or NaN if a match is found but the value is non-numeric.
.. versionadded:: 3.5.0
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('<a><b>1</b><b>2</b></a>',)], ['x'])
>>> df.select(sf.xpath_float(df.x, sf.lit('sum(a/b)'))).show()
+------------------------+
|xpath_float(x, sum(a/b))|
+------------------------+
| 3.0|
+------------------------+
"""
return _invoke_function_over_columns("xpath_float", xml, path)
@_try_remote_functions
def xpath_int(xml: "ColumnOrName", path: "ColumnOrName") -> Column:
"""
Returns an integer value, or the value zero if no match is found,
or a match is found but the value is non-numeric.
.. versionadded:: 3.5.0
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('<a><b>1</b><b>2</b></a>',)], ['x'])
>>> df.select(sf.xpath_int(df.x, sf.lit('sum(a/b)'))).show()
+----------------------+
|xpath_int(x, sum(a/b))|
+----------------------+
| 3|
+----------------------+
"""
return _invoke_function_over_columns("xpath_int", xml, path)
@_try_remote_functions
def xpath_long(xml: "ColumnOrName", path: "ColumnOrName") -> Column:
"""
Returns a long integer value, or the value zero if no match is found,
or a match is found but the value is non-numeric.
.. versionadded:: 3.5.0
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('<a><b>1</b><b>2</b></a>',)], ['x'])
>>> df.select(sf.xpath_long(df.x, sf.lit('sum(a/b)'))).show()
+-----------------------+
|xpath_long(x, sum(a/b))|
+-----------------------+
| 3|
+-----------------------+
"""
return _invoke_function_over_columns("xpath_long", xml, path)
@_try_remote_functions
def xpath_short(xml: "ColumnOrName", path: "ColumnOrName") -> Column:
"""
Returns a short integer value, or the value zero if no match is found,
or a match is found but the value is non-numeric.
.. versionadded:: 3.5.0
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('<a><b>1</b><b>2</b></a>',)], ['x'])
>>> df.select(sf.xpath_short(df.x, sf.lit('sum(a/b)'))).show()
+------------------------+
|xpath_short(x, sum(a/b))|
+------------------------+
| 3|
+------------------------+
"""
return _invoke_function_over_columns("xpath_short", xml, path)
@_try_remote_functions
def xpath_string(xml: "ColumnOrName", path: "ColumnOrName") -> Column:
"""
Returns the text contents of the first xml node that matches the XPath expression.
.. versionadded:: 3.5.0
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('<a><b>b</b><c>cc</c></a>',)], ['x'])
>>> df.select(sf.xpath_string(df.x, sf.lit('a/c'))).show()
+--------------------+
|xpath_string(x, a/c)|
+--------------------+
| cc|
+--------------------+
"""
return _invoke_function_over_columns("xpath_string", xml, path)
@_try_remote_functions
def trunc(date: "ColumnOrName", format: str) -> Column:
"""
Returns date truncated to the unit specified by the format.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
date : :class:`~pyspark.sql.Column` or column name
input column of values to truncate.
format : literal string
'year', 'yyyy', 'yy' to truncate by year,
or 'month', 'mon', 'mm' to truncate by month
Other options are: 'week', 'quarter'
Returns
-------
:class:`~pyspark.sql.Column`
truncated date.
See Also
--------
:meth:`pyspark.sql.functions.date_trunc`
:meth:`pyspark.sql.functions.time_trunc`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('1997-02-28',)], ['dt'])
>>> df.select('*', sf.trunc(df.dt, 'year')).show()
+----------+---------------+
| dt|trunc(dt, year)|
+----------+---------------+
|1997-02-28| 1997-01-01|
+----------+---------------+
>>> df.select('*', sf.trunc('dt', 'mon')).show()
+----------+--------------+
| dt|trunc(dt, mon)|
+----------+--------------+
|1997-02-28| 1997-02-01|
+----------+--------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("trunc", _to_java_column(date), _enum_to_value(format))
@_try_remote_functions
def date_trunc(format: str, timestamp: "ColumnOrName") -> Column:
"""
Returns timestamp truncated to the unit specified by the format.
.. versionadded:: 2.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
format : literal string
'year', 'yyyy', 'yy' to truncate by year,
'month', 'mon', 'mm' to truncate by month,
'day', 'dd' to truncate by day,
Other options are:
'microsecond', 'millisecond', 'second', 'minute', 'hour', 'week', 'quarter'
timestamp : :class:`~pyspark.sql.Column` or column name
input column of values to truncate.
Returns
-------
:class:`~pyspark.sql.Column`
truncated timestamp.
See Also
--------
:meth:`pyspark.sql.functions.trunc`
:meth:`pyspark.sql.functions.time_trunc`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('1997-02-28 05:02:11',)], ['ts'])
>>> df.select('*', sf.date_trunc('year', df.ts)).show()
+-------------------+--------------------+
| ts|date_trunc(year, ts)|
+-------------------+--------------------+
|1997-02-28 05:02:11| 1997-01-01 00:00:00|
+-------------------+--------------------+
>>> df.select('*', sf.date_trunc('mon', 'ts')).show()
+-------------------+-------------------+
| ts|date_trunc(mon, ts)|
+-------------------+-------------------+
|1997-02-28 05:02:11|1997-02-01 00:00:00|
+-------------------+-------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("date_trunc", _enum_to_value(format), _to_java_column(timestamp))
@_try_remote_functions
def next_day(date: "ColumnOrName", dayOfWeek: str) -> Column:
"""
Returns the first date which is later than the value of the date column
based on second `week day` argument.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
date : :class:`~pyspark.sql.Column` or column name
target column to compute on.
dayOfWeek : literal string
day of the week, case-insensitive, accepts:
"Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"
Returns
-------
:class:`~pyspark.sql.Column`
the column of computed results.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2015-07-27',)], ['dt'])
>>> df.select('*', sf.next_day(df.dt, 'Sun')).show()
+----------+-----------------+
| dt|next_day(dt, Sun)|
+----------+-----------------+
|2015-07-27| 2015-08-02|
+----------+-----------------+
>>> df.select('*', sf.next_day('dt', 'Sat')).show()
+----------+-----------------+
| dt|next_day(dt, Sat)|
+----------+-----------------+
|2015-07-27| 2015-08-01|
+----------+-----------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("next_day", _to_java_column(date), _enum_to_value(dayOfWeek))
@_try_remote_functions
def last_day(date: "ColumnOrName") -> Column:
"""
Returns the last day of the month which the given date belongs to.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
date : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
last day of the month.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('1997-02-10',)], ['dt'])
>>> df.select('*', sf.last_day(df.dt)).show()
+----------+------------+
| dt|last_day(dt)|
+----------+------------+
|1997-02-10| 1997-02-28|
+----------+------------+
>>> df.select('*', sf.last_day('dt')).show()
+----------+------------+
| dt|last_day(dt)|
+----------+------------+
|1997-02-10| 1997-02-28|
+----------+------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("last_day", _to_java_column(date))
@_try_remote_functions
def from_unixtime(timestamp: "ColumnOrName", format: str = "yyyy-MM-dd HH:mm:ss") -> Column:
"""
Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
representing the timestamp of that moment in the current system time zone in the given
format.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
timestamp : :class:`~pyspark.sql.Column` or column name
column of unix time values.
format : literal string, optional
format to use to convert to (default: yyyy-MM-dd HH:mm:ss)
Returns
-------
:class:`~pyspark.sql.Column`
formatted timestamp as string.
See Also
--------
:meth:`pyspark.sql.functions.date_from_unix_date`
:meth:`pyspark.sql.functions.unix_seconds`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1428476400,)], ['unix_time'])
>>> df.select('*', sf.from_unixtime('unix_time')).show()
+----------+---------------------------------------------+
| unix_time|from_unixtime(unix_time, yyyy-MM-dd HH:mm:ss)|
+----------+---------------------------------------------+
|1428476400| 2015-04-08 00:00:00|
+----------+---------------------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("from_unixtime", _to_java_column(timestamp), _enum_to_value(format))
@overload
def unix_timestamp(timestamp: "ColumnOrName", format: str = ...) -> Column:
...
@overload
def unix_timestamp() -> Column:
...
@_try_remote_functions
def unix_timestamp(
timestamp: Optional["ColumnOrName"] = None, format: str = "yyyy-MM-dd HH:mm:ss"
) -> Column:
"""
Convert time string with given pattern ('yyyy-MM-dd HH:mm:ss', by default)
to Unix time stamp (in seconds), using the default timezone and the default
locale, returns null if failed.
if `timestamp` is None, then it returns current timestamp.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
timestamp : :class:`~pyspark.sql.Column` or column name, optional
timestamps of string values.
format : literal string, optional
alternative format to use for converting (default: yyyy-MM-dd HH:mm:ss).
Returns
-------
:class:`~pyspark.sql.Column`
unix time as long integer.
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
Example 1: Returns the current timestamp in UNIX.
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.unix_timestamp()).show() # doctest: +SKIP
+----------+
| unix_time|
+----------+
|1702018137|
+----------+
Example 2: Using default format 'yyyy-MM-dd HH:mm:ss' parses the timestamp string.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08 12:12:12',)], ['ts'])
>>> df.select('*', sf.unix_timestamp('ts')).show()
+-------------------+---------------------------------------+
| ts|unix_timestamp(ts, yyyy-MM-dd HH:mm:ss)|
+-------------------+---------------------------------------+
|2015-04-08 12:12:12| 1428520332|
+-------------------+---------------------------------------+
Example 3: Using user-specified format 'yyyy-MM-dd' parses the timestamp string.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
>>> df.select('*', sf.unix_timestamp('dt', 'yyyy-MM-dd')).show()
+----------+------------------------------+
| dt|unix_timestamp(dt, yyyy-MM-dd)|
+----------+------------------------------+
|2015-04-08| 1428476400|
+----------+------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
from pyspark.sql.classic.column import _to_java_column
if timestamp is None:
return _invoke_function("unix_timestamp")
return _invoke_function("unix_timestamp", _to_java_column(timestamp), _enum_to_value(format))
@_try_remote_functions
def from_utc_timestamp(timestamp: "ColumnOrName", tz: Union[Column, str]) -> Column:
"""
This is a common function for databases supporting TIMESTAMP WITHOUT TIMEZONE. This function
takes a timestamp which is timezone-agnostic, and interprets it as a timestamp in UTC, and
renders that timestamp as a timestamp in the given time zone.
However, timestamp in Spark represents number of microseconds from the Unix epoch, which is not
timezone-agnostic. So in Spark this function just shift the timestamp value from UTC timezone to
the given timezone.
This function may return confusing result if the input is a string with timezone, e.g.
'2018-03-13T06:18:23+00:00'. The reason is that, Spark firstly cast the string to timestamp
according to the timezone in the string, and finally display the result by converting the
timestamp to string according to the session local timezone.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
timestamp : :class:`~pyspark.sql.Column` or column name
the column that contains timestamps
tz : :class:`~pyspark.sql.Column` or literal string
A string detailing the time zone ID that the input should be adjusted to. It should
be in the format of either region-based zone IDs or zone offsets. Region IDs must
have the form 'area/city', such as 'America/Los_Angeles'. Zone offsets must be in
the format '(+|-)HH:mm', for example '-08:00' or '+01:00'. Also 'UTC' and 'Z' are
supported as aliases of '+00:00'. Other short names are not recommended to use
because they can be ambiguous.
.. versionchanged:: 2.4
`tz` can take a :class:`~pyspark.sql.Column` containing timezone ID strings.
Returns
-------
:class:`~pyspark.sql.Column`
timestamp value represented in given timezone.
See Also
--------
:meth:`pyspark.sql.functions.to_utc_timestamp`
:meth:`pyspark.sql.functions.to_timestamp`
:meth:`pyspark.sql.functions.to_timestamp_ltz`
:meth:`pyspark.sql.functions.to_timestamp_ntz`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28 10:30:00', 'JST')], ['ts', 'tz'])
>>> df.select('*', sf.from_utc_timestamp('ts', 'PST')).show()
+-------------------+---+---------------------------+
| ts| tz|from_utc_timestamp(ts, PST)|
+-------------------+---+---------------------------+
|1997-02-28 10:30:00|JST| 1997-02-28 02:30:00|
+-------------------+---+---------------------------+
>>> df.select('*', sf.from_utc_timestamp(df.ts, df.tz)).show()
+-------------------+---+--------------------------+
| ts| tz|from_utc_timestamp(ts, tz)|
+-------------------+---+--------------------------+
|1997-02-28 10:30:00|JST| 1997-02-28 19:30:00|
+-------------------+---+--------------------------+
"""
return _invoke_function_over_columns("from_utc_timestamp", timestamp, lit(tz))
@_try_remote_functions
def to_utc_timestamp(timestamp: "ColumnOrName", tz: Union[Column, str]) -> Column:
"""
This is a common function for databases supporting TIMESTAMP WITHOUT TIMEZONE. This function
takes a timestamp which is timezone-agnostic, and interprets it as a timestamp in the given
timezone, and renders that timestamp as a timestamp in UTC.
However, timestamp in Spark represents number of microseconds from the Unix epoch, which is not
timezone-agnostic. So in Spark this function just shift the timestamp value from the given
timezone to UTC timezone.
This function may return confusing result if the input is a string with timezone, e.g.
'2018-03-13T06:18:23+00:00'. The reason is that, Spark firstly cast the string to timestamp
according to the timezone in the string, and finally display the result by converting the
timestamp to string according to the session local timezone.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
timestamp : :class:`~pyspark.sql.Column` or column name
the column that contains timestamps
tz : :class:`~pyspark.sql.Column` or literal string
A string detailing the time zone ID that the input should be adjusted to. It should
be in the format of either region-based zone IDs or zone offsets. Region IDs must
have the form 'area/city', such as 'America/Los_Angeles'. Zone offsets must be in
the format '(+|-)HH:mm', for example '-08:00' or '+01:00'. Also 'UTC' and 'Z' are
supported as aliases of '+00:00'. Other short names are not recommended to use
because they can be ambiguous.
.. versionchanged:: 2.4.0
`tz` can take a :class:`~pyspark.sql.Column` containing timezone ID strings.
Returns
-------
:class:`~pyspark.sql.Column`
timestamp value represented in UTC timezone.
See Also
--------
:meth:`pyspark.sql.functions.from_utc_timestamp`
:meth:`pyspark.sql.functions.to_timestamp`
:meth:`pyspark.sql.functions.to_timestamp_ltz`
:meth:`pyspark.sql.functions.to_timestamp_ntz`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('1997-02-28 10:30:00', 'JST')], ['ts', 'tz'])
>>> df.select('*', sf.to_utc_timestamp('ts', "PST")).show()
+-------------------+---+-------------------------+
| ts| tz|to_utc_timestamp(ts, PST)|
+-------------------+---+-------------------------+
|1997-02-28 10:30:00|JST| 1997-02-28 18:30:00|
+-------------------+---+-------------------------+
>>> df.select('*', sf.to_utc_timestamp(df.ts, df.tz)).show()
+-------------------+---+------------------------+
| ts| tz|to_utc_timestamp(ts, tz)|
+-------------------+---+------------------------+
|1997-02-28 10:30:00|JST| 1997-02-28 01:30:00|
+-------------------+---+------------------------+
"""
return _invoke_function_over_columns("to_utc_timestamp", timestamp, lit(tz))
@_try_remote_functions
def timestamp_seconds(col: "ColumnOrName") -> Column:
"""
Converts the number of seconds from the Unix epoch (1970-01-01T00:00:00Z)
to a timestamp.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
unix time values.
Returns
-------
:class:`~pyspark.sql.Column`
converted timestamp value.
See Also
--------
:meth:`pyspark.sql.functions.timestamp_millis`
:meth:`pyspark.sql.functions.timestamp_micros`
:meth:`pyspark.sql.functions.unix_seconds`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "UTC")
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1230219000,), (1280219000,)], ['seconds'])
>>> df.select('*', sf.timestamp_seconds('seconds')).show()
+----------+--------------------------+
| seconds|timestamp_seconds(seconds)|
+----------+--------------------------+
|1230219000| 2008-12-25 15:30:00|
|1280219000| 2010-07-27 08:23:20|
+----------+--------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
return _invoke_function_over_columns("timestamp_seconds", col)
@_try_remote_functions
def time_trunc(unit: "ColumnOrName", time: "ColumnOrName") -> Column:
"""
Returns `time` truncated to the `unit`.
.. versionadded:: 4.1.0
Parameters
----------
unit : :class:`~pyspark.sql.Column` or column name
The unit to truncate the time to. Supported units are: "HOUR", "MINUTE", "SECOND",
"MILLISECOND", and "MICROSECOND". The unit is case-insensitive.
time : :class:`~pyspark.sql.Column` or column name
A time to truncate.
Returns
-------
:class:`~pyspark.sql.Column`
A time truncated to the specified unit.
See Also
--------
:meth:`pyspark.sql.functions.trunc`
:meth:`pyspark.sql.functions.date_trunc`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("HOUR", "13:08:15")],
... ['unit', 'time']).withColumn("time", sf.col("time").cast("time"))
>>> df.select('*', sf.time_trunc('unit', 'time')).show()
+----+--------+----------------------+
|unit| time|time_trunc(unit, time)|
+----+--------+----------------------+
|HOUR|13:08:15| 13:00:00|
+----+--------+----------------------+
"""
return _invoke_function_over_columns("time_trunc", unit, time)
@_try_remote_functions
def timestamp_millis(col: "ColumnOrName") -> Column:
"""
Creates timestamp from the number of milliseconds since UTC epoch.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
unix time values.
Returns
-------
:class:`~pyspark.sql.Column`
converted timestamp value.
See Also
--------
:meth:`pyspark.sql.functions.timestamp_seconds`
:meth:`pyspark.sql.functions.timestamp_micros`
:meth:`pyspark.sql.functions.unix_millis`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "UTC")
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1230219000,), (1280219000,)], ['millis'])
>>> df.select('*', sf.timestamp_millis('millis')).show()
+----------+------------------------+
| millis|timestamp_millis(millis)|
+----------+------------------------+
|1230219000| 1970-01-15 05:43:39|
|1280219000| 1970-01-15 19:36:59|
+----------+------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
return _invoke_function_over_columns("timestamp_millis", col)
@_try_remote_functions
def timestamp_micros(col: "ColumnOrName") -> Column:
"""
Creates timestamp from the number of microseconds since UTC epoch.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
unix time values.
Returns
-------
:class:`~pyspark.sql.Column`
converted timestamp value.
See Also
--------
:meth:`pyspark.sql.functions.timestamp_seconds`
:meth:`pyspark.sql.functions.timestamp_millis`
:meth:`pyspark.sql.functions.unix_micros`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "UTC")
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1230219000,), (1280219000,)], ['micros'])
>>> df.select('*', sf.timestamp_micros('micros')).show(truncate=False)
+----------+------------------------+
|micros |timestamp_micros(micros)|
+----------+------------------------+
|1230219000|1970-01-01 00:20:30.219 |
|1280219000|1970-01-01 00:21:20.219 |
+----------+------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
return _invoke_function_over_columns("timestamp_micros", col)
@_try_remote_functions
def timestamp_diff(unit: str, start: "ColumnOrName", end: "ColumnOrName") -> Column:
"""
Gets the difference between the timestamps in the specified units by truncating
the fraction part.
.. versionadded:: 4.0.0
Parameters
----------
unit : literal string
This indicates the units of the difference between the given timestamps.
Supported options are (case insensitive): "YEAR", "QUARTER", "MONTH", "WEEK",
"DAY", "HOUR", "MINUTE", "SECOND", "MILLISECOND" and "MICROSECOND".
start : :class:`~pyspark.sql.Column` or column name
A timestamp which the expression subtracts from `endTimestamp`.
end : :class:`~pyspark.sql.Column` or column name
A timestamp from which the expression subtracts `startTimestamp`.
Returns
-------
:class:`~pyspark.sql.Column`
the difference between the timestamps.
See Also
--------
:meth:`pyspark.sql.functions.datediff`
:meth:`pyspark.sql.functions.date_diff`
Examples
--------
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(datetime.datetime(2016, 3, 11, 9, 0, 7), datetime.datetime(2024, 4, 2, 9, 0, 7))],
... ['ts1', 'ts2'])
>>> df.select('*', sf.timestamp_diff('year', 'ts1', 'ts2')).show()
+-------------------+-------------------+-----------------------------+
| ts1| ts2|timestampdiff(year, ts1, ts2)|
+-------------------+-------------------+-----------------------------+
|2016-03-11 09:00:07|2024-04-02 09:00:07| 8|
+-------------------+-------------------+-----------------------------+
>>> df.select('*', sf.timestamp_diff('WEEK', 'ts1', 'ts2')).show()
+-------------------+-------------------+-----------------------------+
| ts1| ts2|timestampdiff(WEEK, ts1, ts2)|
+-------------------+-------------------+-----------------------------+
|2016-03-11 09:00:07|2024-04-02 09:00:07| 420|
+-------------------+-------------------+-----------------------------+
>>> df.select('*', sf.timestamp_diff('day', df.ts2, df.ts1)).show()
+-------------------+-------------------+----------------------------+
| ts1| ts2|timestampdiff(day, ts2, ts1)|
+-------------------+-------------------+----------------------------+
|2016-03-11 09:00:07|2024-04-02 09:00:07| -2944|
+-------------------+-------------------+----------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"timestamp_diff",
_enum_to_value(unit),
_to_java_column(start),
_to_java_column(end),
)
@_try_remote_functions
def timestamp_add(unit: str, quantity: "ColumnOrName", ts: "ColumnOrName") -> Column:
"""
Gets the difference between the timestamps in the specified units by truncating
the fraction part.
.. versionadded:: 4.0.0
Parameters
----------
unit : literal string
This indicates the units of the difference between the given timestamps.
Supported options are (case insensitive): "YEAR", "QUARTER", "MONTH", "WEEK",
"DAY", "HOUR", "MINUTE", "SECOND", "MILLISECOND" and "MICROSECOND".
quantity : :class:`~pyspark.sql.Column` or column name
The number of units of time that you want to add.
ts : :class:`~pyspark.sql.Column` or column name
A timestamp to which you want to add.
Returns
-------
:class:`~pyspark.sql.Column`
the difference between the timestamps.
See Also
--------
:meth:`pyspark.sql.functions.dateadd`
:meth:`pyspark.sql.functions.date_add`
Examples
--------
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [(datetime.datetime(2016, 3, 11, 9, 0, 7), 2),
... (datetime.datetime(2024, 4, 2, 9, 0, 7), 3)], ['ts', 'quantity'])
>>> df.select('*', sf.timestamp_add('year', 'quantity', 'ts')).show()
+-------------------+--------+--------------------------------+
| ts|quantity|timestampadd(year, quantity, ts)|
+-------------------+--------+--------------------------------+
|2016-03-11 09:00:07| 2| 2018-03-11 09:00:07|
|2024-04-02 09:00:07| 3| 2027-04-02 09:00:07|
+-------------------+--------+--------------------------------+
>>> df.select('*', sf.timestamp_add('WEEK', sf.lit(5), df.ts)).show()
+-------------------+--------+-------------------------+
| ts|quantity|timestampadd(WEEK, 5, ts)|
+-------------------+--------+-------------------------+
|2016-03-11 09:00:07| 2| 2016-04-15 09:00:07|
|2024-04-02 09:00:07| 3| 2024-05-07 09:00:07|
+-------------------+--------+-------------------------+
>>> df.select('*', sf.timestamp_add('day', sf.lit(-5), 'ts')).show()
+-------------------+--------+-------------------------+
| ts|quantity|timestampadd(day, -5, ts)|
+-------------------+--------+-------------------------+
|2016-03-11 09:00:07| 2| 2016-03-06 09:00:07|
|2024-04-02 09:00:07| 3| 2024-03-28 09:00:07|
+-------------------+--------+-------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"timestamp_add",
_enum_to_value(unit),
_to_java_column(quantity),
_to_java_column(ts),
)
@_try_remote_functions
def window(
timeColumn: "ColumnOrName",
windowDuration: str,
slideDuration: Optional[str] = None,
startTime: Optional[str] = None,
) -> Column:
"""Bucketize rows into one or more time windows given a timestamp specifying column. Window
starts are inclusive but the window ends are exclusive, e.g. 12:05 will be in the window
[12:05,12:10) but not in [12:00,12:05). Windows can support microsecond precision. Windows in
the order of months are not supported.
The time column must be of :class:`pyspark.sql.types.TimestampType`.
Durations are provided as strings, e.g. '1 second', '1 day 12 hours', '2 minutes'. Valid
interval strings are 'week', 'day', 'hour', 'minute', 'second', 'millisecond', 'microsecond'.
If the ``slideDuration`` is not provided, the windows will be tumbling windows.
The startTime is the offset with respect to 1970-01-01 00:00:00 UTC with which to start
window intervals. For example, in order to have hourly tumbling windows that start 15 minutes
past the hour, e.g. 12:15-13:15, 13:15-14:15... provide `startTime` as `15 minutes`.
The output column will be a struct called 'window' by default with the nested columns 'start'
and 'end', where 'start' and 'end' will be of :class:`pyspark.sql.types.TimestampType`.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
timeColumn : :class:`~pyspark.sql.Column` or column name
The column or the expression to use as the timestamp for windowing by time.
The time column must be of TimestampType or TimestampNTZType.
windowDuration : literal string
A string specifying the width of the window, e.g. `10 minutes`,
`1 second`. Check `org.apache.spark.unsafe.types.CalendarInterval` for
valid duration identifiers. Note that the duration is a fixed length of
time, and does not vary over time according to a calendar. For example,
`1 day` always means 86,400,000 milliseconds, not a calendar day.
slideDuration : literal string, optional
A new window will be generated every `slideDuration`. Must be less than
or equal to the `windowDuration`. Check
`org.apache.spark.unsafe.types.CalendarInterval` for valid duration
identifiers. This duration is likewise absolute, and does not vary
according to a calendar.
startTime : literal string, optional
The offset with respect to 1970-01-01 00:00:00 UTC with which to start
window intervals. For example, in order to have hourly tumbling windows that
start 15 minutes past the hour, e.g. 12:15-13:15, 13:15-14:15... provide
`startTime` as `15 minutes`.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
See Also
--------
:meth:`pyspark.sql.functions.window_time`
:meth:`pyspark.sql.functions.session_window`
Examples
--------
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(datetime.datetime(2016, 3, 11, 9, 0, 7), 1)], ['dt', 'v'])
>>> df2 = df.groupBy(sf.window('dt', '5 seconds')).agg(sf.sum('v'))
>>> df2.show(truncate=False)
+------------------------------------------+------+
|window |sum(v)|
+------------------------------------------+------+
|{2016-03-11 09:00:05, 2016-03-11 09:00:10}|1 |
+------------------------------------------+------+
>>> df2.printSchema()
root
|-- window: struct (nullable = false)
| |-- start: timestamp (nullable = true)
| |-- end: timestamp (nullable = true)
|-- sum(v): long (nullable = true)
"""
from pyspark.sql.classic.column import _to_java_column
def check_string_field(field, fieldName): # type: ignore[no-untyped-def]
if not field or type(field) is not str:
raise PySparkTypeError(
errorClass="NOT_STR",
messageParameters={"arg_name": fieldName, "arg_type": type(field).__name__},
)
windowDuration = _enum_to_value(windowDuration)
slideDuration = _enum_to_value(slideDuration)
startTime = _enum_to_value(startTime)
time_col = _to_java_column(timeColumn)
check_string_field(windowDuration, "windowDuration")
if slideDuration and startTime:
check_string_field(slideDuration, "slideDuration")
check_string_field(startTime, "startTime")
return _invoke_function("window", time_col, windowDuration, slideDuration, startTime)
elif slideDuration:
check_string_field(slideDuration, "slideDuration")
return _invoke_function("window", time_col, windowDuration, slideDuration)
elif startTime:
check_string_field(startTime, "startTime")
return _invoke_function("window", time_col, windowDuration, windowDuration, startTime)
else:
return _invoke_function("window", time_col, windowDuration)
@_try_remote_functions
def window_time(
windowColumn: "ColumnOrName",
) -> Column:
"""Computes the event time from a window column. The column window values are produced
by window aggregating operators and are of type `STRUCT<start: TIMESTAMP, end: TIMESTAMP>`
where start is inclusive and end is exclusive. The event time of records produced by window
aggregating operators can be computed as ``window_time(window)`` and are
``window.end - lit(1).alias("microsecond")`` (as microsecond is the minimal supported event
time precision). The window column must be one produced by a window aggregating operator.
.. versionadded:: 3.4.0
Parameters
----------
windowColumn : :class:`~pyspark.sql.Column` or column name
The window column of a window aggregate records.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
See Also
--------
:meth:`pyspark.sql.functions.window`
:meth:`pyspark.sql.functions.session_window`
Examples
--------
>>> import datetime
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(datetime.datetime(2016, 3, 11, 9, 0, 7), 1)], ['dt', 'v'])
Group the data into 5 second time windows and aggregate as sum.
>>> df2 = df.groupBy(sf.window('dt', '5 seconds')).agg(sf.sum('v'))
Extract the window event time using the window_time function.
>>> df2.select('*', sf.window_time('window')).show(truncate=False)
+------------------------------------------+------+--------------------------+
|window |sum(v)|window_time(window) |
+------------------------------------------+------+--------------------------+
|{2016-03-11 09:00:05, 2016-03-11 09:00:10}|1 |2016-03-11 09:00:09.999999|
+------------------------------------------+------+--------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
window_col = _to_java_column(windowColumn)
return _invoke_function("window_time", window_col)
@_try_remote_functions
def session_window(timeColumn: "ColumnOrName", gapDuration: Union[Column, str]) -> Column:
"""
Generates session window given a timestamp specifying column.
Session window is one of dynamic windows, which means the length of window is varying
according to the given inputs. The length of session window is defined as "the timestamp
of latest input of the session + gap duration", so when the new inputs are bound to the
current session window, the end time of session window can be expanded according to the new
inputs.
Windows can support microsecond precision. Windows in the order of months are not supported.
For a streaming query, you may use the function `current_timestamp` to generate windows on
processing time.
gapDuration is provided as strings, e.g. '1 second', '1 day 12 hours', '2 minutes'. Valid
interval strings are 'week', 'day', 'hour', 'minute', 'second', 'millisecond', 'microsecond'.
It could also be a Column which can be evaluated to gap duration dynamically based on the
input row.
The output column will be a struct called 'session_window' by default with the nested columns
'start' and 'end', where 'start' and 'end' will be of :class:`pyspark.sql.types.TimestampType`.
.. versionadded:: 3.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
timeColumn : :class:`~pyspark.sql.Column` or column name
The column name or column to use as the timestamp for windowing by time.
The time column must be of TimestampType or TimestampNTZType.
gapDuration : :class:`~pyspark.sql.Column` or literal string
A Python string literal or column specifying the timeout of the session. It could be
static value, e.g. `10 minutes`, `1 second`, or an expression/UDF that specifies gap
duration dynamically based on the input row.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
See Also
--------
:meth:`pyspark.sql.functions.window`
:meth:`pyspark.sql.functions.window_time`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('2016-03-11 09:00:07', 1)], ['dt', 'v'])
>>> df2 = df.groupBy(sf.session_window('dt', '5 seconds')).agg(sf.sum('v'))
>>> df2.show(truncate=False)
+------------------------------------------+------+
|session_window |sum(v)|
+------------------------------------------+------+
|{2016-03-11 09:00:07, 2016-03-11 09:00:12}|1 |
+------------------------------------------+------+
>>> df2.printSchema()
root
|-- session_window: struct (nullable = false)
| |-- start: timestamp (nullable = true)
| |-- end: timestamp (nullable = true)
|-- sum(v): long (nullable = true)
"""
from pyspark.sql.classic.column import _to_java_column
def check_field(field: Union[Column, str], fieldName: str) -> None:
if field is None or not isinstance(field, (str, Column)):
raise PySparkTypeError(
errorClass="NOT_COLUMN_OR_STR",
messageParameters={"arg_name": fieldName, "arg_type": type(field).__name__},
)
time_col = _to_java_column(timeColumn)
gapDuration = _enum_to_value(gapDuration)
check_field(gapDuration, "gapDuration")
gap_duration = gapDuration if isinstance(gapDuration, str) else _to_java_column(gapDuration)
return _invoke_function("session_window", time_col, gap_duration)
@_try_remote_functions
def to_unix_timestamp(
timestamp: "ColumnOrName",
format: Optional["ColumnOrName"] = None,
) -> Column:
"""
Returns the UNIX timestamp of the given time.
.. versionadded:: 3.5.0
Parameters
----------
timestamp : :class:`~pyspark.sql.Column` or column name
Input column or strings.
format : :class:`~pyspark.sql.Column` or column name, optional
format to use to convert UNIX timestamp values.
See Also
--------
:meth:`pyspark.sql.functions.to_date`
:meth:`pyspark.sql.functions.to_timestamp`
:meth:`pyspark.sql.functions.to_timestamp_ltz`
:meth:`pyspark.sql.functions.to_timestamp_ntz`
:meth:`pyspark.sql.functions.to_utc_timestamp`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
Example 1: Using default format to parse the timestamp string.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08 12:12:12',)], ['ts'])
>>> df.select('*', sf.to_unix_timestamp('ts')).show()
+-------------------+------------------------------------------+
| ts|to_unix_timestamp(ts, yyyy-MM-dd HH:mm:ss)|
+-------------------+------------------------------------------+
|2015-04-08 12:12:12| 1428520332|
+-------------------+------------------------------------------+
Example 2: Using user-specified format 'yyyy-MM-dd' to parse the date string.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
>>> df.select('*', sf.to_unix_timestamp(df.dt, sf.lit('yyyy-MM-dd'))).show()
+----------+---------------------------------+
| dt|to_unix_timestamp(dt, yyyy-MM-dd)|
+----------+---------------------------------+
|2015-04-08| 1428476400|
+----------+---------------------------------+
Example 3: Using a format column to represent different formats.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame(
... [('2015-04-08', 'yyyy-MM-dd'), ('2025+01+09', 'yyyy+MM+dd')], ['dt', 'fmt'])
>>> df.select('*', sf.to_unix_timestamp('dt', 'fmt')).show()
+----------+----------+--------------------------+
| dt| fmt|to_unix_timestamp(dt, fmt)|
+----------+----------+--------------------------+
|2015-04-08|yyyy-MM-dd| 1428476400|
|2025+01+09|yyyy+MM+dd| 1736409600|
+----------+----------+--------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
if format is not None:
return _invoke_function_over_columns("to_unix_timestamp", timestamp, format)
else:
return _invoke_function_over_columns("to_unix_timestamp", timestamp)
@_try_remote_functions
def to_timestamp_ltz(
timestamp: "ColumnOrName",
format: Optional["ColumnOrName"] = None,
) -> Column:
"""
Parses the `timestamp` with the `format` to a timestamp with time zone.
Returns null with invalid input.
.. versionadded:: 3.5.0
Parameters
----------
timestamp : :class:`~pyspark.sql.Column` or column name
Input column or strings.
format : :class:`~pyspark.sql.Column` or column name, optional
format to use to convert type `TimestampType` timestamp values.
See Also
--------
:meth:`pyspark.sql.functions.to_date`
:meth:`pyspark.sql.functions.to_timestamp`
:meth:`pyspark.sql.functions.to_timestamp_ntz`
:meth:`pyspark.sql.functions.to_utc_timestamp`
:meth:`pyspark.sql.functions.to_unix_timestamp`
:meth:`pyspark.sql.functions.date_format`
Examples
--------
Example 1: Using default format to parse the timestamp string.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08 12:12:12',)], ['ts'])
>>> df.select('*', sf.to_timestamp_ltz('ts')).show()
+-------------------+--------------------+
| ts|to_timestamp_ltz(ts)|
+-------------------+--------------------+
|2015-04-08 12:12:12| 2015-04-08 12:12:12|
+-------------------+--------------------+
Example 2: Using user-specified format to parse the date string.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2016-12-31',)], ['dt'])
>>> df.select('*', sf.to_timestamp_ltz(df.dt, sf.lit('yyyy-MM-dd'))).show()
+----------+--------------------------------+
| dt|to_timestamp_ltz(dt, yyyy-MM-dd)|
+----------+--------------------------------+
|2016-12-31| 2016-12-31 00:00:00|
+----------+--------------------------------+
Example 3: Using a format column to represent different formats.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame(
... [('2015-04-08', 'yyyy-MM-dd'), ('2025+01+09', 'yyyy+MM+dd')], ['dt', 'fmt'])
>>> df.select('*', sf.to_timestamp_ltz('dt', 'fmt')).show()
+----------+----------+-------------------------+
| dt| fmt|to_timestamp_ltz(dt, fmt)|
+----------+----------+-------------------------+
|2015-04-08|yyyy-MM-dd| 2015-04-08 00:00:00|
|2025+01+09|yyyy+MM+dd| 2025-01-09 00:00:00|
+----------+----------+-------------------------+
"""
if format is not None:
return _invoke_function_over_columns("to_timestamp_ltz", timestamp, format)
else:
return _invoke_function_over_columns("to_timestamp_ltz", timestamp)
@_try_remote_functions
def to_timestamp_ntz(
timestamp: "ColumnOrName",
format: Optional["ColumnOrName"] = None,
) -> Column:
"""
Parses the `timestamp` with the `format` to a timestamp without time zone.
Returns null with invalid input.
.. versionadded:: 3.5.0
Parameters
----------
timestamp : :class:`~pyspark.sql.Column` or column name
Input column or strings.
format : :class:`~pyspark.sql.Column` or column name, optional
format to use to convert type `TimestampNTZType` timestamp values.
See Also
--------
:meth:`pyspark.sql.functions.to_date`
:meth:`pyspark.sql.functions.to_timestamp`
:meth:`pyspark.sql.functions.to_timestamp_ltz`
:meth:`pyspark.sql.functions.to_utc_timestamp`
:meth:`pyspark.sql.functions.to_unix_timestamp`
:meth:`pyspark.sql.functions.date_format`
Examples
--------
Example 1: Using default format to parse the timestamp string.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08 12:12:12',)], ['ts'])
>>> df.select('*', sf.to_timestamp_ntz('ts')).show()
+-------------------+--------------------+
| ts|to_timestamp_ntz(ts)|
+-------------------+--------------------+
|2015-04-08 12:12:12| 2015-04-08 12:12:12|
+-------------------+--------------------+
Example 2: Using user-specified format 'yyyy-MM-dd' to parse the date string.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2016-12-31',)], ['dt'])
>>> df.select('*', sf.to_timestamp_ntz(df.dt, sf.lit('yyyy-MM-dd'))).show()
+----------+--------------------------------+
| dt|to_timestamp_ntz(dt, yyyy-MM-dd)|
+----------+--------------------------------+
|2016-12-31| 2016-12-31 00:00:00|
+----------+--------------------------------+
Example 3: Using a format column to represent different formats.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame(
... [('2015-04-08', 'yyyy-MM-dd'), ('2025+01+09', 'yyyy+MM+dd')], ['dt', 'fmt'])
>>> df.select('*', sf.to_timestamp_ntz('dt', 'fmt')).show()
+----------+----------+-------------------------+
| dt| fmt|to_timestamp_ntz(dt, fmt)|
+----------+----------+-------------------------+
|2015-04-08|yyyy-MM-dd| 2015-04-08 00:00:00|
|2025+01+09|yyyy+MM+dd| 2025-01-09 00:00:00|
+----------+----------+-------------------------+
"""
if format is not None:
return _invoke_function_over_columns("to_timestamp_ntz", timestamp, format)
else:
return _invoke_function_over_columns("to_timestamp_ntz", timestamp)
# ---------------------------- misc functions ----------------------------------
@_try_remote_functions
def current_catalog() -> Column:
"""Returns the current catalog.
.. versionadded:: 3.5.0
See Also
--------
:meth:`pyspark.sql.functions.current_database`
:meth:`pyspark.sql.functions.current_schema`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.current_catalog()).show()
+-----------------+
|current_catalog()|
+-----------------+
| spark_catalog|
+-----------------+
"""
return _invoke_function("current_catalog")
@_try_remote_functions
def current_database() -> Column:
"""Returns the current database.
.. versionadded:: 3.5.0
See Also
--------
:meth:`pyspark.sql.functions.current_catalog`
:meth:`pyspark.sql.functions.current_schema`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.current_database()).show()
+----------------+
|current_schema()|
+----------------+
| default|
+----------------+
"""
return _invoke_function("current_database")
@_try_remote_functions
def current_schema() -> Column:
"""Returns the current database.
.. versionadded:: 3.5.0
See Also
--------
:meth:`pyspark.sql.functions.current_catalog`
:meth:`pyspark.sql.functions.current_database`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.current_schema()).show()
+----------------+
|current_schema()|
+----------------+
| default|
+----------------+
"""
return _invoke_function("current_schema")
@_try_remote_functions
def current_user() -> Column:
"""Returns the current database.
.. versionadded:: 3.5.0
See Also
--------
:meth:`pyspark.sql.functions.user`
:meth:`pyspark.sql.functions.session_user`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.current_user()).show() # doctest: +SKIP
+--------------+
|current_user()|
+--------------+
| ruifeng.zheng|
+--------------+
"""
return _invoke_function("current_user")
@_try_remote_functions
def user() -> Column:
"""Returns the current database.
.. versionadded:: 3.5.0
See Also
--------
:meth:`pyspark.sql.functions.current_user`
:meth:`pyspark.sql.functions.session_user`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.user()).show() # doctest: +SKIP
+--------------+
| user()|
+--------------+
| ruifeng.zheng|
+--------------+
"""
return _invoke_function("user")
@_try_remote_functions
def session_user() -> Column:
"""Returns the user name of current execution context.
.. versionadded:: 4.0.0
See Also
--------
:meth:`pyspark.sql.functions.user`
:meth:`pyspark.sql.functions.current_user`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.session_user()).show() # doctest: +SKIP
+--------------+
|session_user()|
+--------------+
| ruifeng.zheng|
+--------------+
"""
return _invoke_function("session_user")
@_try_remote_functions
def uuid(seed: Optional[Union[Column, int]] = None) -> Column:
"""Returns an universally unique identifier (UUID) string.
The value is returned as a canonical UUID 36-character string.
.. versionadded:: 4.1.0
Parameters
----------
seed : :class:`~pyspark.sql.Column` or int
Optional random number seed to use.
Examples
--------
Example 1: Generate UUIDs with random seed
>>> from pyspark.sql import functions as sf
>>> spark.range(5).select(sf.uuid()).show(truncate=False) # doctest: +SKIP
+------------------------------------+
|uuid() |
+------------------------------------+
|627ae05e-b319-42b5-b4e4-71c8c9754dd1|
|f781cce5-a2e2-464d-bc8b-426ff448e404|
|15e2e66e-8416-4ea2-af3c-409363408189|
|fb1d6178-7676-4791-baa9-f2ddcc494515|
|d48665e8-2657-4c6b-b7c8-8ae0cd646e41|
+------------------------------------+
Example 2: Generate UUIDs with a specified seed
>>> from pyspark.sql import functions as sf
>>> spark.range(0, 5, 1, 1).select(sf.uuid(seed=123)).show(truncate=False)
+------------------------------------+
|uuid() |
+------------------------------------+
|4c99192d-23d6-4d88-b814-a634398120f0|
|af506873-3c53-41e3-8354-a24856b8de8a|
|7b4b370e-e867-47e2-93c0-f6990463a12d|
|1c4d1733-ff1a-4a6c-b144-0b0345adf0d0|
|7478f235-f8bc-4112-8e59-a28f50e46890|
+------------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
if seed is None:
return _invoke_function("uuid")
else:
return _invoke_function("uuid", _to_java_column(lit(seed)))
@_try_remote_functions
def crc32(col: "ColumnOrName") -> Column:
"""
Calculates the cyclic redundancy check value (CRC32) of a binary column and
returns the value as a bigint.
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
.. versionadded:: 1.5.0
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('ABC',)], ['a'])
>>> df.select('*', sf.crc32('a')).show(truncate=False)
+---+----------+
|a |crc32(a) |
+---+----------+
|ABC|2743272264|
+---+----------+
"""
return _invoke_function_over_columns("crc32", col)
@_try_remote_functions
def md5(col: "ColumnOrName") -> Column:
"""Calculates the MD5 digest and returns the value as a 32 character hex string.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('ABC',)], ['a'])
>>> df.select('*', sf.md5('a')).show(truncate=False)
+---+--------------------------------+
|a |md5(a) |
+---+--------------------------------+
|ABC|902fbdd2b1df0c4f70b4a5d23525e932|
+---+--------------------------------+
"""
return _invoke_function_over_columns("md5", col)
@_try_remote_functions
def sha1(col: "ColumnOrName") -> Column:
"""Returns the hex string result of SHA-1.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
See Also
--------
:meth:`pyspark.sql.functions.sha`
:meth:`pyspark.sql.functions.sha2`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('ABC',)], ['a'])
>>> df.select('*', sf.sha1('a')).show(truncate=False)
+---+----------------------------------------+
|a |sha1(a) |
+---+----------------------------------------+
|ABC|3c01bdbb26f358bab27f267924aa2c9a03fcfdb8|
+---+----------------------------------------+
"""
return _invoke_function_over_columns("sha1", col)
@_try_remote_functions
def sha2(col: "ColumnOrName", numBits: int) -> Column:
"""Returns the hex string result of SHA-2 family of hash functions (SHA-224, SHA-256, SHA-384,
and SHA-512). The numBits indicates the desired bit length of the result, which must have a
value of 224, 256, 384, 512, or 0 (which is equivalent to 256).
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to compute on.
numBits : int
the desired bit length of the result, which must have a
value of 224, 256, 384, 512, or 0 (which is equivalent to 256).
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
See Also
--------
:meth:`pyspark.sql.functions.sha`
:meth:`pyspark.sql.functions.sha1`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([['Alice'], ['Bob']], ['name'])
>>> df.select('*', sf.sha2('name', 256)).show(truncate=False)
+-----+----------------------------------------------------------------+
|name |sha2(name, 256) |
+-----+----------------------------------------------------------------+
|Alice|3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043|
|Bob |cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961|
+-----+----------------------------------------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
if numBits not in [0, 224, 256, 384, 512]:
raise PySparkValueError(
errorClass="VALUE_NOT_ALLOWED",
messageParameters={
"arg_name": "numBits",
"allowed_values": "[0, 224, 256, 384, 512]",
},
)
return _invoke_function("sha2", _to_java_column(col), numBits)
@_try_remote_functions
def hash(*cols: "ColumnOrName") -> Column:
"""Calculates the hash code of given columns, and returns the result as an int column.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : :class:`~pyspark.sql.Column` or column name
one or more columns to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
hash value as int column.
See Also
--------
:meth:`pyspark.sql.functions.xxhash64`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('ABC', 'DEF')], ['c1', 'c2'])
>>> df.select('*', sf.hash('c1')).show()
+---+---+----------+
| c1| c2| hash(c1)|
+---+---+----------+
|ABC|DEF|-757602832|
+---+---+----------+
>>> df.select('*', sf.hash('c1', df.c2)).show()
+---+---+------------+
| c1| c2|hash(c1, c2)|
+---+---+------------+
|ABC|DEF| 599895104|
+---+---+------------+
>>> df.select('*', sf.hash('*')).show()
+---+---+------------+
| c1| c2|hash(c1, c2)|
+---+---+------------+
|ABC|DEF| 599895104|
+---+---+------------+
"""
return _invoke_function_over_seq_of_columns("hash", cols)
@_try_remote_functions
def xxhash64(*cols: "ColumnOrName") -> Column:
"""Calculates the hash code of given columns using the 64-bit variant of the xxHash algorithm,
and returns the result as a long column. The hash computation uses an initial seed of 42.
.. versionadded:: 3.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : :class:`~pyspark.sql.Column` or column name
one or more columns to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
hash value as long column.
See Also
--------
:meth:`pyspark.sql.functions.hash`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('ABC', 'DEF')], ['c1', 'c2'])
>>> df.select('*', sf.xxhash64('c1')).show()
+---+---+-------------------+
| c1| c2| xxhash64(c1)|
+---+---+-------------------+
|ABC|DEF|4105715581806190027|
+---+---+-------------------+
>>> df.select('*', sf.xxhash64('c1', df.c2)).show()
+---+---+-------------------+
| c1| c2| xxhash64(c1, c2)|
+---+---+-------------------+
|ABC|DEF|3233247871021311208|
+---+---+-------------------+
>>> df.select('*', sf.xxhash64('*')).show()
+---+---+-------------------+
| c1| c2| xxhash64(c1, c2)|
+---+---+-------------------+
|ABC|DEF|3233247871021311208|
+---+---+-------------------+
"""
return _invoke_function_over_seq_of_columns("xxhash64", cols)
@_try_remote_functions
def assert_true(col: "ColumnOrName", errMsg: Optional[Union[Column, str]] = None) -> Column:
"""
Returns `null` if the input column is `true`; throws an exception
with the provided error message otherwise.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
column name or column that represents the input column to test
errMsg : :class:`~pyspark.sql.Column` or literal string, optional
A Python string literal or column containing the error message
Returns
-------
:class:`~pyspark.sql.Column`
`null` if the input column is `true` otherwise throws an error with specified message.
See Also
--------
:meth:`pyspark.sql.functions.raise_error`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(0, 1)], ['a', 'b'])
>>> df.select('*', sf.assert_true(df.a < df.b)).show() # doctest: +SKIP
+------------------------------------------------------+
|assert_true((a < b), '(a#788L < b#789L)' is not true!)|
+------------------------------------------------------+
| NULL|
+------------------------------------------------------+
>>> df.select('*', sf.assert_true(df.a < df.b, df.a)).show()
+---+---+-----------------------+
| a| b|assert_true((a < b), a)|
+---+---+-----------------------+
| 0| 1| NULL|
+---+---+-----------------------+
>>> df.select('*', sf.assert_true(df.a < df.b, 'error')).show()
+---+---+---------------------------+
| a| b|assert_true((a < b), error)|
+---+---+---------------------------+
| 0| 1| NULL|
+---+---+---------------------------+
>>> df.select('*', sf.assert_true(df.a > df.b, 'My error msg')).show() # doctest: +SKIP
...
java.lang.RuntimeException: My error msg
...
"""
errMsg = _enum_to_value(errMsg)
if errMsg is None:
return _invoke_function_over_columns("assert_true", col)
if not isinstance(errMsg, (str, Column)):
raise PySparkTypeError(
errorClass="NOT_COLUMN_OR_STR",
messageParameters={"arg_name": "errMsg", "arg_type": type(errMsg).__name__},
)
return _invoke_function_over_columns("assert_true", col, lit(errMsg))
@_try_remote_functions
def raise_error(errMsg: Union[Column, str]) -> Column:
"""
Throws an exception with the provided error message.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
errMsg : :class:`~pyspark.sql.Column` or literal string
A Python string literal or column containing the error message
Returns
-------
:class:`~pyspark.sql.Column`
throws an error with specified message.
See Also
--------
:meth:`pyspark.sql.functions.assert_true`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.raise_error("My error message")).show() # doctest: +SKIP
...
java.lang.RuntimeException: My error message
...
"""
errMsg = _enum_to_value(errMsg)
if not isinstance(errMsg, (str, Column)):
raise PySparkTypeError(
errorClass="NOT_COLUMN_OR_STR",
messageParameters={"arg_name": "errMsg", "arg_type": type(errMsg).__name__},
)
return _invoke_function_over_columns("raise_error", lit(errMsg))
# ---------------------- String/Binary functions ------------------------------
@_try_remote_functions
def upper(col: "ColumnOrName") -> Column:
"""
Converts a string expression to upper case.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
upper case values.
See Also
--------
:meth:`pyspark.sql.functions.lower`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(["Spark", "PySpark", "Pandas API"], "STRING")
>>> df.select("*", sf.upper("value")).show()
+----------+------------+
| value|upper(value)|
+----------+------------+
| Spark| SPARK|
| PySpark| PYSPARK|
|Pandas API| PANDAS API|
+----------+------------+
"""
return _invoke_function_over_columns("upper", col)
@_try_remote_functions
def lower(col: "ColumnOrName") -> Column:
"""
Converts a string expression to lower case.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
lower case values.
See Also
--------
:meth:`pyspark.sql.functions.upper`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(["Spark", "PySpark", "Pandas API"], "STRING")
>>> df.select("*", sf.lower("value")).show()
+----------+------------+
| value|lower(value)|
+----------+------------+
| Spark| spark|
| PySpark| pyspark|
|Pandas API| pandas api|
+----------+------------+
"""
return _invoke_function_over_columns("lower", col)
@_try_remote_functions
def ascii(col: "ColumnOrName") -> Column:
"""
Computes the numeric value of the first character of the string column.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
numeric value.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(["Spark", "PySpark", "Pandas API"], "STRING")
>>> df.select("*", sf.ascii("value")).show()
+----------+------------+
| value|ascii(value)|
+----------+------------+
| Spark| 83|
| PySpark| 80|
|Pandas API| 80|
+----------+------------+
"""
return _invoke_function_over_columns("ascii", col)
@_try_remote_functions
def base64(col: "ColumnOrName") -> Column:
"""
Computes the BASE64 encoding of a binary column and returns it as a string column.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
BASE64 encoding of string value.
See Also
--------
:meth:`pyspark.sql.functions.unbase64`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(["Spark", "PySpark", "Pandas API"], "STRING")
>>> df.select("*", sf.base64("value")).show()
+----------+----------------+
| value| base64(value)|
+----------+----------------+
| Spark| U3Bhcms=|
| PySpark| UHlTcGFyaw==|
|Pandas API|UGFuZGFzIEFQSQ==|
+----------+----------------+
"""
return _invoke_function_over_columns("base64", col)
@_try_remote_functions
def unbase64(col: "ColumnOrName") -> Column:
"""
Decodes a BASE64 encoded string column and returns it as a binary column.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
encoded string value.
See Also
--------
:meth:`pyspark.sql.functions.base64`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(["U3Bhcms=", "UHlTcGFyaw==", "UGFuZGFzIEFQSQ=="], "STRING")
>>> df.select("*", sf.unbase64("value")).show(truncate=False)
+----------------+-------------------------------+
|value |unbase64(value) |
+----------------+-------------------------------+
|U3Bhcms= |[53 70 61 72 6B] |
|UHlTcGFyaw== |[50 79 53 70 61 72 6B] |
|UGFuZGFzIEFQSQ==|[50 61 6E 64 61 73 20 41 50 49]|
+----------------+-------------------------------+
"""
return _invoke_function_over_columns("unbase64", col)
@_try_remote_functions
def ltrim(col: "ColumnOrName", trim: Optional["ColumnOrName"] = None) -> Column:
"""
Trim the spaces from left end for the specified string value.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
trim : :class:`~pyspark.sql.Column` or column name, optional
The trim string characters to trim, the default value is a single space
.. versionadded:: 4.0.0
Returns
-------
:class:`~pyspark.sql.Column`
left trimmed values.
See Also
--------
:meth:`pyspark.sql.functions.trim`
:meth:`pyspark.sql.functions.rtrim`
Examples
--------
Example 1: Trim the spaces
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([" Spark", "Spark ", " Spark"], "STRING")
>>> df.select("*", sf.ltrim("value")).show()
+--------+------------+
| value|ltrim(value)|
+--------+------------+
| Spark| Spark|
| Spark | Spark |
| Spark| Spark|
+--------+------------+
Example 2: Trim specified characters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(["***Spark", "Spark**", "*Spark"], "STRING")
>>> df.select("*", sf.ltrim("value", sf.lit("*"))).show()
+--------+--------------------------+
| value|TRIM(LEADING * FROM value)|
+--------+--------------------------+
|***Spark| Spark|
| Spark**| Spark**|
| *Spark| Spark|
+--------+--------------------------+
Example 3: Trim a column containing different characters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("**Spark*", "*"), ("==Spark=", "=")], ["value", "t"])
>>> df.select("*", sf.ltrim("value", "t")).show()
+--------+---+--------------------------+
| value| t|TRIM(LEADING t FROM value)|
+--------+---+--------------------------+
|**Spark*| *| Spark*|
|==Spark=| =| Spark=|
+--------+---+--------------------------+
"""
if trim is not None:
return _invoke_function_over_columns("ltrim", col, trim)
else:
return _invoke_function_over_columns("ltrim", col)
@_try_remote_functions
def rtrim(col: "ColumnOrName", trim: Optional["ColumnOrName"] = None) -> Column:
"""
Trim the spaces from right end for the specified string value.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
trim : :class:`~pyspark.sql.Column` or column name, optional
The trim string characters to trim, the default value is a single space
.. versionadded:: 4.0.0
Returns
-------
:class:`~pyspark.sql.Column`
right trimmed values.
See Also
--------
:meth:`pyspark.sql.functions.trim`
:meth:`pyspark.sql.functions.ltrim`
Examples
--------
Example 1: Trim the spaces
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([" Spark", "Spark ", " Spark"], "STRING")
>>> df.select("*", sf.rtrim("value")).show()
+--------+------------+
| value|rtrim(value)|
+--------+------------+
| Spark| Spark|
| Spark | Spark|
| Spark| Spark|
+--------+------------+
Example 2: Trim specified characters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(["***Spark", "Spark**", "*Spark"], "STRING")
>>> df.select("*", sf.rtrim("value", sf.lit("*"))).show()
+--------+---------------------------+
| value|TRIM(TRAILING * FROM value)|
+--------+---------------------------+
|***Spark| ***Spark|
| Spark**| Spark|
| *Spark| *Spark|
+--------+---------------------------+
Example 3: Trim a column containing different characters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("**Spark*", "*"), ("==Spark=", "=")], ["value", "t"])
>>> df.select("*", sf.rtrim("value", "t")).show()
+--------+---+---------------------------+
| value| t|TRIM(TRAILING t FROM value)|
+--------+---+---------------------------+
|**Spark*| *| **Spark|
|==Spark=| =| ==Spark|
+--------+---+---------------------------+
"""
if trim is not None:
return _invoke_function_over_columns("rtrim", col, trim)
else:
return _invoke_function_over_columns("rtrim", col)
@_try_remote_functions
def trim(col: "ColumnOrName", trim: Optional["ColumnOrName"] = None) -> Column:
"""
Trim the spaces from both ends for the specified string column.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
trim : :class:`~pyspark.sql.Column` or column name, optional
The trim string characters to trim, the default value is a single space
.. versionadded:: 4.0.0
Returns
-------
:class:`~pyspark.sql.Column`
trimmed values from both sides.
See Also
--------
:meth:`pyspark.sql.functions.ltrim`
:meth:`pyspark.sql.functions.rtrim`
Examples
--------
Example 1: Trim the spaces
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([" Spark", "Spark ", " Spark"], "STRING")
>>> df.select("*", sf.trim("value")).show()
+--------+-----------+
| value|trim(value)|
+--------+-----------+
| Spark| Spark|
| Spark | Spark|
| Spark| Spark|
+--------+-----------+
Example 2: Trim specified characters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(["***Spark", "Spark**", "*Spark"], "STRING")
>>> df.select("*", sf.trim("value", sf.lit("*"))).show()
+--------+-----------------------+
| value|TRIM(BOTH * FROM value)|
+--------+-----------------------+
|***Spark| Spark|
| Spark**| Spark|
| *Spark| Spark|
+--------+-----------------------+
Example 3: Trim a column containing different characters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("**Spark*", "*"), ("==Spark=", "=")], ["value", "t"])
>>> df.select("*", sf.trim("value", "t")).show()
+--------+---+-----------------------+
| value| t|TRIM(BOTH t FROM value)|
+--------+---+-----------------------+
|**Spark*| *| Spark|
|==Spark=| =| Spark|
+--------+---+-----------------------+
"""
if trim is not None:
return _invoke_function_over_columns("trim", col, trim)
else:
return _invoke_function_over_columns("trim", col)
@_try_remote_functions
def concat_ws(sep: str, *cols: "ColumnOrName") -> Column:
"""
Concatenates multiple input string columns together into a single string column,
using the given separator.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
sep : literal string
words separator.
cols : :class:`~pyspark.sql.Column` or column name
list of columns to work on.
Returns
-------
:class:`~pyspark.sql.Column`
string of concatenated words.
See Also
--------
:meth:`pyspark.sql.functions.concat`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("abcd", "123")], ["s", "d"])
>>> df.select("*", sf.concat_ws("-", df.s, "d", sf.lit("xyz"))).show()
+----+---+-----------------------+
| s| d|concat_ws(-, s, d, xyz)|
+----+---+-----------------------+
|abcd|123| abcd-123-xyz|
+----+---+-----------------------+
"""
from pyspark.sql.classic.column import _to_seq, _to_java_column
sc = _get_active_spark_context()
return _invoke_function("concat_ws", _enum_to_value(sep), _to_seq(sc, cols, _to_java_column))
@_try_remote_functions
def decode(col: "ColumnOrName", charset: str) -> Column:
"""
Computes the first argument into a string from a binary using the provided character set
(one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16', 'UTF-32').
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
charset : literal string
charset to use to decode to.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
See Also
--------
:meth:`pyspark.sql.functions.encode`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(b"\x61\x62\x63\x64",)], ["a"])
>>> df.select("*", sf.decode("a", "UTF-8")).show()
+-------------+----------------+
| a|decode(a, UTF-8)|
+-------------+----------------+
|[61 62 63 64]| abcd|
+-------------+----------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("decode", _to_java_column(col), _enum_to_value(charset))
@_try_remote_functions
def encode(col: "ColumnOrName", charset: str) -> Column:
"""
Computes the first argument into a binary from a string using the provided character set
(one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16', 'UTF-32').
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
charset : literal string
charset to use to encode.
Returns
-------
:class:`~pyspark.sql.Column`
the column for computed results.
See Also
--------
:meth:`pyspark.sql.functions.decode`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("abcd",)], ["c"])
>>> df.select("*", sf.encode("c", "UTF-8")).show()
+----+----------------+
| c|encode(c, UTF-8)|
+----+----------------+
|abcd| [61 62 63 64]|
+----+----------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("encode", _to_java_column(col), _enum_to_value(charset))
@_try_remote_functions
def is_valid_utf8(str: "ColumnOrName") -> Column:
"""
Returns true if the input is a valid UTF-8 string, otherwise returns false.
.. versionadded:: 4.0.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
A column of strings, each representing a UTF-8 byte sequence.
Returns
-------
:class:`~pyspark.sql.Column`
whether the input string is a valid UTF-8 string.
See Also
--------
:meth:`pyspark.sql.functions.make_valid_utf8`
:meth:`pyspark.sql.functions.validate_utf8`
:meth:`pyspark.sql.functions.try_validate_utf8`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.is_valid_utf8(sf.lit("SparkSQL"))).show()
+-----------------------+
|is_valid_utf8(SparkSQL)|
+-----------------------+
| true|
+-----------------------+
"""
return _invoke_function_over_columns("is_valid_utf8", str)
@_try_remote_functions
def make_valid_utf8(str: "ColumnOrName") -> Column:
"""
Returns a new string in which all invalid UTF-8 byte sequences, if any, are replaced by the
Unicode replacement character (U+FFFD).
.. versionadded:: 4.0.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
A column of strings, each representing a UTF-8 byte sequence.
Returns
-------
:class:`~pyspark.sql.Column`
the valid UTF-8 version of the given input string.
See Also
--------
:meth:`pyspark.sql.functions.is_valid_utf8`
:meth:`pyspark.sql.functions.validate_utf8`
:meth:`pyspark.sql.functions.try_validate_utf8`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.make_valid_utf8(sf.lit("SparkSQL"))).show()
+-------------------------+
|make_valid_utf8(SparkSQL)|
+-------------------------+
| SparkSQL|
+-------------------------+
"""
return _invoke_function_over_columns("make_valid_utf8", str)
@_try_remote_functions
def validate_utf8(str: "ColumnOrName") -> Column:
"""
Returns the input value if it corresponds to a valid UTF-8 string, or emits an error otherwise.
.. versionadded:: 4.0.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
A column of strings, each representing a UTF-8 byte sequence.
Returns
-------
:class:`~pyspark.sql.Column`
the input string if it is a valid UTF-8 string, error otherwise.
See Also
--------
:meth:`pyspark.sql.functions.is_valid_utf8`
:meth:`pyspark.sql.functions.make_valid_utf8`
:meth:`pyspark.sql.functions.try_validate_utf8`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.validate_utf8(sf.lit("SparkSQL"))).show()
+-----------------------+
|validate_utf8(SparkSQL)|
+-----------------------+
| SparkSQL|
+-----------------------+
"""
return _invoke_function_over_columns("validate_utf8", str)
@_try_remote_functions
def try_validate_utf8(str: "ColumnOrName") -> Column:
"""
Returns the input value if it corresponds to a valid UTF-8 string, or NULL otherwise.
.. versionadded:: 4.0.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
A column of strings, each representing a UTF-8 byte sequence.
Returns
-------
:class:`~pyspark.sql.Column`
the input string if it is a valid UTF-8 string, null otherwise.
See Also
--------
:meth:`pyspark.sql.functions.is_valid_utf8`
:meth:`pyspark.sql.functions.make_valid_utf8`
:meth:`pyspark.sql.functions.validate_utf8`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.try_validate_utf8(sf.lit("SparkSQL"))).show()
+---------------------------+
|try_validate_utf8(SparkSQL)|
+---------------------------+
| SparkSQL|
+---------------------------+
"""
return _invoke_function_over_columns("try_validate_utf8", str)
@_try_remote_functions
def format_number(col: "ColumnOrName", d: int) -> Column:
"""
Formats the number X to a format like '#,--#,--#.--', rounded to d decimal places
with HALF_EVEN round mode, and returns the result as a string.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
the column name of the numeric value to be formatted
d : int
the N decimal places
Returns
-------
:class:`~pyspark.sql.Column`
the column of formatted results.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(5,)], ["a"])
>>> df.select("*", sf.format_number("a", 4), sf.format_number(df.a, 6)).show()
+---+-------------------+-------------------+
| a|format_number(a, 4)|format_number(a, 6)|
+---+-------------------+-------------------+
| 5| 5.0000| 5.000000|
+---+-------------------+-------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("format_number", _to_java_column(col), _enum_to_value(d))
@_try_remote_functions
def format_string(format: str, *cols: "ColumnOrName") -> Column:
"""
Formats the arguments in printf-style and returns the result as a string column.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
format : literal string
string that can contain embedded format tags and used as result column's value
cols : :class:`~pyspark.sql.Column` or column name
column names or :class:`~pyspark.sql.Column`\\s to be used in formatting
Returns
-------
:class:`~pyspark.sql.Column`
the column of formatted results.
See Also
--------
:meth:`pyspark.sql.functions.printf`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(5, "hello")], ["a", "b"])
>>> df.select("*", sf.format_string('%d %s', "a", df.b)).show()
+---+-----+--------------------------+
| a| b|format_string(%d %s, a, b)|
+---+-----+--------------------------+
| 5|hello| 5 hello|
+---+-----+--------------------------+
"""
from pyspark.sql.classic.column import _to_seq, _to_java_column
sc = _get_active_spark_context()
return _invoke_function(
"format_string", _enum_to_value(format), _to_seq(sc, cols, _to_java_column)
)
@_try_remote_functions
def instr(str: "ColumnOrName", substr: Union[Column, str]) -> Column:
"""
Locate the position of the first occurrence of substr column in the given string.
Returns null if either of the arguments are null.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The position is not zero based, but 1 based index. Returns 0 if substr
could not be found in str.
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
target column to work on.
substr : :class:`~pyspark.sql.Column` or literal string
substring to look for.
.. versionchanged:: 4.0.0
`substr` now accepts column.
Returns
-------
:class:`~pyspark.sql.Column`
location of the first occurrence of the substring as integer.
See Also
--------
:meth:`pyspark.sql.functions.locate`
:meth:`pyspark.sql.functions.substr`
:meth:`pyspark.sql.functions.substring`
:meth:`pyspark.sql.functions.substring_index`
Examples
--------
Example 1: Using a literal string as the 'substring'
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("abcd",), ("xyz",)], ["s",])
>>> df.select("*", sf.instr(df.s, "b")).show()
+----+-----------+
| s|instr(s, b)|
+----+-----------+
|abcd| 2|
| xyz| 0|
+----+-----------+
Example 2: Using a Column 'substring'
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("abcd",), ("xyz",)], ["s",])
>>> df.select("*", sf.instr("s", sf.lit("abc").substr(0, 2))).show()
+----+---------------------------+
| s|instr(s, substr(abc, 0, 2))|
+----+---------------------------+
|abcd| 1|
| xyz| 0|
+----+---------------------------+
"""
return _invoke_function_over_columns("instr", str, lit(substr))
@_try_remote_functions
def overlay(
src: "ColumnOrName",
replace: "ColumnOrName",
pos: Union["ColumnOrName", int],
len: Union["ColumnOrName", int] = -1,
) -> Column:
"""
Overlay the specified portion of `src` with `replace`,
starting from byte position `pos` of `src` and proceeding for `len` bytes.
.. versionadded:: 3.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
src : :class:`~pyspark.sql.Column` or column name
the string that will be replaced
replace : :class:`~pyspark.sql.Column` or column name
the substitution string
pos : :class:`~pyspark.sql.Column` or column name or int
the starting position in src
len : :class:`~pyspark.sql.Column` or column name or int, optional
the number of bytes to replace in src
string by 'replace' defaults to -1, which represents the length of the 'replace' string
Returns
-------
:class:`~pyspark.sql.Column`
string with replaced values.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("SPARK_SQL", "CORE")], ("x", "y"))
>>> df.select("*", sf.overlay("x", df.y, 7)).show()
+---------+----+--------------------+
| x| y|overlay(x, y, 7, -1)|
+---------+----+--------------------+
|SPARK_SQL|CORE| SPARK_CORE|
+---------+----+--------------------+
>>> df.select("*", sf.overlay("x", df.y, 7, 0)).show()
+---------+----+-------------------+
| x| y|overlay(x, y, 7, 0)|
+---------+----+-------------------+
|SPARK_SQL|CORE| SPARK_CORESQL|
+---------+----+-------------------+
>>> df.select("*", sf.overlay("x", "y", 7, 2)).show()
+---------+----+-------------------+
| x| y|overlay(x, y, 7, 2)|
+---------+----+-------------------+
|SPARK_SQL|CORE| SPARK_COREL|
+---------+----+-------------------+
"""
pos = _enum_to_value(pos)
if not isinstance(pos, (int, str, Column)):
raise PySparkTypeError(
errorClass="NOT_COLUMN_OR_INT_OR_STR",
messageParameters={"arg_name": "pos", "arg_type": type(pos).__name__},
)
len = _enum_to_value(len)
if len is not None and not isinstance(len, (int, str, Column)):
raise PySparkTypeError(
errorClass="NOT_COLUMN_OR_INT_OR_STR",
messageParameters={"arg_name": "len", "arg_type": type(len).__name__},
)
if isinstance(pos, int):
pos = lit(pos)
if isinstance(len, int):
len = lit(len)
return _invoke_function_over_columns("overlay", src, replace, pos, len)
@_try_remote_functions
def sentences(
string: "ColumnOrName",
language: Optional["ColumnOrName"] = None,
country: Optional["ColumnOrName"] = None,
) -> Column:
"""
Splits a string into arrays of sentences, where each sentence is an array of words.
The `language` and `country` arguments are optional,
When they are omitted:
1.If they are both omitted, the `Locale.ROOT - locale(language='', country='')` is used.
The `Locale.ROOT` is regarded as the base locale of all locales, and is used as the
language/country neutral locale for the locale sensitive operations.
2.If the `country` is omitted, the `locale(language, country='')` is used.
When they are null:
1.If they are both `null`, the `Locale.US - locale(language='en', country='US')` is used.
2.If the `language` is null and the `country` is not null,
the `Locale.US - locale(language='en', country='US')` is used.
3.If the `language` is not null and the `country` is null, the `locale(language)` is used.
4.If neither is `null`, the `locale(language, country)` is used.
.. versionadded:: 3.2.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. versionchanged:: 4.0.0
Supports `sentences(string, language)`.
Parameters
----------
string : :class:`~pyspark.sql.Column` or column name
a string to be split
language : :class:`~pyspark.sql.Column` or column name, optional
a language of the locale
country : :class:`~pyspark.sql.Column` or column name, optional
a country of the locale
Returns
-------
:class:`~pyspark.sql.Column`
arrays of split sentences.
See Also
--------
:meth:`pyspark.sql.functions.split`
:meth:`pyspark.sql.functions.split_part`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("This is an example sentence.", )], ["s"])
>>> df.select("*", sf.sentences(df.s, sf.lit("en"), sf.lit("US"))).show(truncate=False)
+----------------------------+-----------------------------------+
|s |sentences(s, en, US) |
+----------------------------+-----------------------------------+
|This is an example sentence.|[[This, is, an, example, sentence]]|
+----------------------------+-----------------------------------+
>>> df.select("*", sf.sentences(df.s, sf.lit("en"))).show(truncate=False)
+----------------------------+-----------------------------------+
|s |sentences(s, en, ) |
+----------------------------+-----------------------------------+
|This is an example sentence.|[[This, is, an, example, sentence]]|
+----------------------------+-----------------------------------+
>>> df.select("*", sf.sentences(df.s)).show(truncate=False)
+----------------------------+-----------------------------------+
|s |sentences(s, , ) |
+----------------------------+-----------------------------------+
|This is an example sentence.|[[This, is, an, example, sentence]]|
+----------------------------+-----------------------------------+
"""
if language is None:
language = lit("")
if country is None:
country = lit("")
return _invoke_function_over_columns("sentences", string, language, country)
@_try_remote_functions
def substring(
str: "ColumnOrName",
pos: Union["ColumnOrName", int],
len: Union["ColumnOrName", int],
) -> Column:
"""
Substring starts at `pos` and is of length `len` when str is String type or
returns the slice of byte array that starts at `pos` in byte and is of length `len`
when str is Binary type.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The position is not zero based, but 1 based index.
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
target column to work on.
pos : :class:`~pyspark.sql.Column` or column name or int
starting position in str.
.. versionchanged:: 4.0.0
`pos` now accepts column and column name.
len : :class:`~pyspark.sql.Column` or column name or int
length of chars.
.. versionchanged:: 4.0.0
`len` now accepts column and column name.
Returns
-------
:class:`~pyspark.sql.Column`
substring of given value.
See Also
--------
:meth:`pyspark.sql.functions.instr`
:meth:`pyspark.sql.functions.locate`
:meth:`pyspark.sql.functions.substr`
:meth:`pyspark.sql.functions.substring_index`
:meth:`pyspark.sql.Column.substr`
Examples
--------
Example 1: Using literal integers as arguments
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('abcd',)], ['s',])
>>> df.select('*', sf.substring(df.s, 1, 2)).show()
+----+------------------+
| s|substring(s, 1, 2)|
+----+------------------+
|abcd| ab|
+----+------------------+
Example 2: Using columns as arguments
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('Spark', 2, 3)], ['s', 'p', 'l'])
>>> df.select('*', sf.substring(df.s, 2, df.l)).show()
+-----+---+---+------------------+
| s| p| l|substring(s, 2, l)|
+-----+---+---+------------------+
|Spark| 2| 3| par|
+-----+---+---+------------------+
>>> df.select('*', sf.substring(df.s, df.p, 3)).show()
+-----+---+---+------------------+
| s| p| l|substring(s, p, 3)|
+-----+---+---+------------------+
|Spark| 2| 3| par|
+-----+---+---+------------------+
>>> df.select('*', sf.substring(df.s, df.p, df.l)).show()
+-----+---+---+------------------+
| s| p| l|substring(s, p, l)|
+-----+---+---+------------------+
|Spark| 2| 3| par|
+-----+---+---+------------------+
Example 3: Using column names as arguments
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('Spark', 2, 3)], ['s', 'p', 'l'])
>>> df.select('*', sf.substring(df.s, 2, 'l')).show()
+-----+---+---+------------------+
| s| p| l|substring(s, 2, l)|
+-----+---+---+------------------+
|Spark| 2| 3| par|
+-----+---+---+------------------+
>>> df.select('*', sf.substring('s', 'p', 'l')).show()
+-----+---+---+------------------+
| s| p| l|substring(s, p, l)|
+-----+---+---+------------------+
|Spark| 2| 3| par|
+-----+---+---+------------------+
"""
pos = _enum_to_value(pos)
pos = lit(pos) if isinstance(pos, int) else pos
len = _enum_to_value(len)
len = lit(len) if isinstance(len, int) else len
return _invoke_function_over_columns("substring", str, pos, len)
@_try_remote_functions
def substring_index(str: "ColumnOrName", delim: str, count: int) -> Column:
"""
Returns the substring from string str before count occurrences of the delimiter delim.
If count is positive, everything the left of the final delimiter (counting from left) is
returned. If count is negative, every to the right of the final delimiter (counting from the
right) is returned. substring_index performs a case-sensitive match when searching for delim.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
target column to work on.
delim : literal string
delimiter of values.
count : int
number of occurrences.
Returns
-------
:class:`~pyspark.sql.Column`
substring of given value.
See Also
--------
:meth:`pyspark.sql.functions.instr`
:meth:`pyspark.sql.functions.locate`
:meth:`pyspark.sql.functions.substr`
:meth:`pyspark.sql.functions.substring`
:meth:`pyspark.sql.Column.substr`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('a.b.c.d',)], ['s'])
>>> df.select('*', sf.substring_index(df.s, '.', 2)).show()
+-------+------------------------+
| s|substring_index(s, ., 2)|
+-------+------------------------+
|a.b.c.d| a.b|
+-------+------------------------+
>>> df.select('*', sf.substring_index('s', '.', -3)).show()
+-------+-------------------------+
| s|substring_index(s, ., -3)|
+-------+-------------------------+
|a.b.c.d| b.c.d|
+-------+-------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"substring_index", _to_java_column(str), _enum_to_value(delim), _enum_to_value(count)
)
@_try_remote_functions
def levenshtein(
left: "ColumnOrName", right: "ColumnOrName", threshold: Optional[int] = None
) -> Column:
"""Computes the Levenshtein distance of the two given strings.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
left : :class:`~pyspark.sql.Column` or column name
first column value.
right : :class:`~pyspark.sql.Column` or column name
second column value.
threshold : int, optional
if set when the levenshtein distance of the two given strings
less than or equal to a given threshold then return result distance, or -1
.. versionadded: 3.5.0
Returns
-------
:class:`~pyspark.sql.Column`
Levenshtein distance as integer value.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('kitten', 'sitting',)], ['l', 'r'])
>>> df.select('*', sf.levenshtein('l', 'r')).show()
+------+-------+-----------------+
| l| r|levenshtein(l, r)|
+------+-------+-----------------+
|kitten|sitting| 3|
+------+-------+-----------------+
>>> df.select('*', sf.levenshtein(df.l, df.r, 2)).show()
+------+-------+--------------------+
| l| r|levenshtein(l, r, 2)|
+------+-------+--------------------+
|kitten|sitting| -1|
+------+-------+--------------------+
"""
from pyspark.sql.classic.column import _to_java_column
if threshold is None:
return _invoke_function_over_columns("levenshtein", left, right)
else:
return _invoke_function(
"levenshtein", _to_java_column(left), _to_java_column(right), _enum_to_value(threshold)
)
@_try_remote_functions
def locate(substr: str, str: "ColumnOrName", pos: int = 1) -> Column:
"""
Locate the position of the first occurrence of substr in a string column, after position pos.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
substr : literal string
a string
str : :class:`~pyspark.sql.Column` or column name
a Column of :class:`pyspark.sql.types.StringType`
pos : int, optional
start position (zero based)
Returns
-------
:class:`~pyspark.sql.Column`
position of the substring.
Notes
-----
The position is not zero based, but 1 based index. Returns 0 if substr
could not be found in str.
See Also
--------
:meth:`pyspark.sql.functions.instr`
:meth:`pyspark.sql.functions.substr`
:meth:`pyspark.sql.functions.substring`
:meth:`pyspark.sql.functions.substring_index`
:meth:`pyspark.sql.Column.substr`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('abcd',)], ['s',])
>>> df.select('*', sf.locate('b', 's', 1)).show()
+----+---------------+
| s|locate(b, s, 1)|
+----+---------------+
|abcd| 2|
+----+---------------+
>>> df.select('*', sf.locate('b', df.s, 3)).show()
+----+---------------+
| s|locate(b, s, 3)|
+----+---------------+
|abcd| 0|
+----+---------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"locate", _enum_to_value(substr), _to_java_column(str), _enum_to_value(pos)
)
@_try_remote_functions
def lpad(
col: "ColumnOrName",
len: Union[Column, int],
pad: Union[Column, str],
) -> Column:
"""
Left-pad the string column to width `len` with `pad`.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
len : :class:`~pyspark.sql.Column` or int
length of the final string.
.. versionchanged:: 4.0.0
`pattern` now accepts column.
pad : :class:`~pyspark.sql.Column` or literal string
chars to prepend.
.. versionchanged:: 4.0.0
`pattern` now accepts column.
Returns
-------
:class:`~pyspark.sql.Column`
left padded result.
See Also
--------
:meth:`pyspark.sql.functions.rpad`
Examples
--------
Example 1: Pad with a literal string
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('abcd',), ('xyz',), ('12',)], ['s',])
>>> df.select("*", sf.lpad(df.s, 6, '#')).show()
+----+-------------+
| s|lpad(s, 6, #)|
+----+-------------+
|abcd| ##abcd|
| xyz| ###xyz|
| 12| ####12|
+----+-------------+
Example 2: Pad with a bytes column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('abcd',), ('xyz',), ('12',)], ['s',])
>>> df.select("*", sf.lpad(df.s, 6, sf.lit(b"\x75\x76"))).show()
+----+-------------------+
| s|lpad(s, 6, X'7576')|
+----+-------------------+
|abcd| uvabcd|
| xyz| uvuxyz|
| 12| uvuv12|
+----+-------------------+
"""
return _invoke_function_over_columns("lpad", col, lit(len), lit(pad))
@_try_remote_functions
def rpad(
col: "ColumnOrName",
len: Union[Column, int],
pad: Union[Column, str],
) -> Column:
"""
Right-pad the string column to width `len` with `pad`.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
target column to work on.
len : :class:`~pyspark.sql.Column` or int
length of the final string.
.. versionchanged:: 4.0.0
`pattern` now accepts column.
pad : :class:`~pyspark.sql.Column` or literal string
chars to prepend.
.. versionchanged:: 4.0.0
`pattern` now accepts column.
Returns
-------
:class:`~pyspark.sql.Column`
right padded result.
See Also
--------
:meth:`pyspark.sql.functions.lpad`
Examples
--------
Example 1: Pad with a literal string
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('abcd',), ('xyz',), ('12',)], ['s',])
>>> df.select("*", sf.rpad(df.s, 6, '#')).show()
+----+-------------+
| s|rpad(s, 6, #)|
+----+-------------+
|abcd| abcd##|
| xyz| xyz###|
| 12| 12####|
+----+-------------+
Example 2: Pad with a bytes column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('abcd',), ('xyz',), ('12',)], ['s',])
>>> df.select("*", sf.rpad(df.s, 6, sf.lit(b"\x75\x76"))).show()
+----+-------------------+
| s|rpad(s, 6, X'7576')|
+----+-------------------+
|abcd| abcduv|
| xyz| xyzuvu|
| 12| 12uvuv|
+----+-------------------+
"""
return _invoke_function_over_columns("rpad", col, lit(len), lit(pad))
@_try_remote_functions
def repeat(col: "ColumnOrName", n: Union["ColumnOrName", int]) -> Column:
"""
Repeats a string column n times, and returns it as a new string column.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
n : :class:`~pyspark.sql.Column` or column name or int
number of times to repeat value.
.. versionchanged:: 4.0.0
`n` now accepts column and column name.
Returns
-------
:class:`~pyspark.sql.Column`
string with repeated values.
Examples
--------
Example 1: Repeat with a constant number of times
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('ab',)], ['s',])
>>> df.select("*", sf.repeat("s", 3)).show()
+---+------------+
| s|repeat(s, 3)|
+---+------------+
| ab| ababab|
+---+------------+
>>> df.select("*", sf.repeat(df.s, sf.lit(4))).show()
+---+------------+
| s|repeat(s, 4)|
+---+------------+
| ab| abababab|
+---+------------+
Example 2: Repeat with a column containing different number of times
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('ab', 5,), ('abc', 6,)], ['s', 't'])
>>> df.select("*", sf.repeat("s", "t")).show()
+---+---+------------------+
| s| t| repeat(s, t)|
+---+---+------------------+
| ab| 5| ababababab|
|abc| 6|abcabcabcabcabcabc|
+---+---+------------------+
"""
n = _enum_to_value(n)
n = lit(n) if isinstance(n, int) else n
return _invoke_function_over_columns("repeat", col, n)
@_try_remote_functions
def split(
str: "ColumnOrName",
pattern: Union[Column, str],
limit: Union["ColumnOrName", int] = -1,
) -> Column:
"""
Splits str around matches of the given pattern.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
a string expression to split
pattern : :class:`~pyspark.sql.Column` or literal string
a string representing a regular expression. The regex string should be
a Java regular expression.
.. versionchanged:: 4.0.0
`pattern` now accepts column. Does not accept column name since string type remain
accepted as a regular expression representation, for backwards compatibility.
In addition to int, `limit` now accepts column and column name.
limit : :class:`~pyspark.sql.Column` or column name or int
an integer which controls the number of times `pattern` is applied.
* ``limit > 0``: The resulting array's length will not be more than `limit`, and the
resulting array's last entry will contain all input beyond the last
matched pattern.
* ``limit <= 0``: `pattern` will be applied as many times as possible, and the resulting
array can be of any size.
.. versionchanged:: 3.0
`split` now takes an optional `limit` field. If not provided, default limit value is -1.
Returns
-------
:class:`~pyspark.sql.Column`
array of separated strings.
See Also
--------
:meth:`pyspark.sql.functions.sentences`
:meth:`pyspark.sql.functions.split_part`
Examples
--------
Example 1: Repeat with a constant pattern
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('oneAtwoBthreeC',)], ['s',])
>>> df.select('*', sf.split(df.s, '[ABC]')).show()
+--------------+-------------------+
| s|split(s, [ABC], -1)|
+--------------+-------------------+
|oneAtwoBthreeC|[one, two, three, ]|
+--------------+-------------------+
>>> df.select('*', sf.split(df.s, '[ABC]', 2)).show()
+--------------+------------------+
| s|split(s, [ABC], 2)|
+--------------+------------------+
|oneAtwoBthreeC| [one, twoBthreeC]|
+--------------+------------------+
>>> df.select('*', sf.split('s', '[ABC]', -2)).show()
+--------------+-------------------+
| s|split(s, [ABC], -2)|
+--------------+-------------------+
|oneAtwoBthreeC|[one, two, three, ]|
+--------------+-------------------+
Example 2: Repeat with a column containing different patterns and limits
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([
... ('oneAtwoBthreeC', '[ABC]', 2),
... ('1A2B3C', '[1-9]+', 1),
... ('aa2bb3cc4', '[1-9]+', -1)], ['s', 'p', 'l'])
>>> df.select('*', sf.split(df.s, df.p)).show()
+--------------+------+---+-------------------+
| s| p| l| split(s, p, -1)|
+--------------+------+---+-------------------+
|oneAtwoBthreeC| [ABC]| 2|[one, two, three, ]|
| 1A2B3C|[1-9]+| 1| [, A, B, C]|
| aa2bb3cc4|[1-9]+| -1| [aa, bb, cc, ]|
+--------------+------+---+-------------------+
>>> df.select(sf.split('s', df.p, 'l')).show()
+-----------------+
| split(s, p, l)|
+-----------------+
|[one, twoBthreeC]|
| [1A2B3C]|
| [aa, bb, cc, ]|
+-----------------+
"""
limit = _enum_to_value(limit)
limit = lit(limit) if isinstance(limit, int) else limit
return _invoke_function_over_columns("split", str, lit(pattern), limit)
@_try_remote_functions
def rlike(str: "ColumnOrName", regexp: "ColumnOrName") -> Column:
r"""Returns true if `str` matches the Java regex `regexp`, or false otherwise.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
target column to work on.
regexp : :class:`~pyspark.sql.Column` or column name
regex pattern to apply.
Returns
-------
:class:`~pyspark.sql.Column`
true if `str` matches a Java regex, or false otherwise.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("1a 2b 14m", r"(\d+)")], ["str", "regexp"])
>>> df.select('*', sf.rlike('str', sf.lit(r'(\d+)'))).show()
+---------+------+-----------------+
| str|regexp|RLIKE(str, (\d+))|
+---------+------+-----------------+
|1a 2b 14m| (\d+)| true|
+---------+------+-----------------+
>>> df.select('*', sf.rlike('str', sf.lit(r'\d{2}b'))).show()
+---------+------+------------------+
| str|regexp|RLIKE(str, \d{2}b)|
+---------+------+------------------+
|1a 2b 14m| (\d+)| false|
+---------+------+------------------+
>>> df.select('*', sf.rlike("str", sf.col("regexp"))).show()
+---------+------+------------------+
| str|regexp|RLIKE(str, regexp)|
+---------+------+------------------+
|1a 2b 14m| (\d+)| true|
+---------+------+------------------+
>>> df.select('*', sf.rlike("str", "regexp")).show()
+---------+------+------------------+
| str|regexp|RLIKE(str, regexp)|
+---------+------+------------------+
|1a 2b 14m| (\d+)| true|
+---------+------+------------------+
"""
return _invoke_function_over_columns("rlike", str, regexp)
@_try_remote_functions
def regexp(str: "ColumnOrName", regexp: "ColumnOrName") -> Column:
r"""Returns true if `str` matches the Java regex `regexp`, or false otherwise.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
target column to work on.
regexp : :class:`~pyspark.sql.Column` or str
regex pattern to apply.
Returns
-------
:class:`~pyspark.sql.Column`
true if `str` matches a Java regex, or false otherwise.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [("1a 2b 14m", r"(\d+)")], ["str", "regexp"]
... ).select(sf.regexp('str', sf.lit(r'(\d+)'))).show()
+------------------+
|REGEXP(str, (\d+))|
+------------------+
| true|
+------------------+
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [("1a 2b 14m", r"(\d+)")], ["str", "regexp"]
... ).select(sf.regexp('str', sf.lit(r'\d{2}b'))).show()
+-------------------+
|REGEXP(str, \d{2}b)|
+-------------------+
| false|
+-------------------+
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [("1a 2b 14m", r"(\d+)")], ["str", "regexp"]
... ).select(sf.regexp('str', sf.col("regexp"))).show()
+-------------------+
|REGEXP(str, regexp)|
+-------------------+
| true|
+-------------------+
"""
return _invoke_function_over_columns("regexp", str, regexp)
@_try_remote_functions
def regexp_like(str: "ColumnOrName", regexp: "ColumnOrName") -> Column:
r"""Returns true if `str` matches the Java regex `regexp`, or false otherwise.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
target column to work on.
regexp : :class:`~pyspark.sql.Column` or str
regex pattern to apply.
Returns
-------
:class:`~pyspark.sql.Column`
true if `str` matches a Java regex, or false otherwise.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [("1a 2b 14m", r"(\d+)")], ["str", "regexp"]
... ).select(sf.regexp_like('str', sf.lit(r'(\d+)'))).show()
+-----------------------+
|REGEXP_LIKE(str, (\d+))|
+-----------------------+
| true|
+-----------------------+
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [("1a 2b 14m", r"(\d+)")], ["str", "regexp"]
... ).select(sf.regexp_like('str', sf.lit(r'\d{2}b'))).show()
+------------------------+
|REGEXP_LIKE(str, \d{2}b)|
+------------------------+
| false|
+------------------------+
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [("1a 2b 14m", r"(\d+)")], ["str", "regexp"]
... ).select(sf.regexp_like('str', sf.col("regexp"))).show()
+------------------------+
|REGEXP_LIKE(str, regexp)|
+------------------------+
| true|
+------------------------+
"""
return _invoke_function_over_columns("regexp_like", str, regexp)
@_try_remote_functions
def randstr(length: Union[Column, int], seed: Optional[Union[Column, int]] = None) -> Column:
"""Returns a string of the specified length whose characters are chosen uniformly at random from
the following pool of characters: 0-9, a-z, A-Z. The random seed is optional. The string length
must be a constant two-byte or four-byte integer (SMALLINT or INT, respectively).
.. versionadded:: 4.0.0
Parameters
----------
length : :class:`~pyspark.sql.Column` or int
Number of characters in the string to generate.
seed : :class:`~pyspark.sql.Column` or int
Optional random number seed to use.
Returns
-------
:class:`~pyspark.sql.Column`
The generated random string with the specified length.
See Also
--------
:meth:`pyspark.sql.functions.rand`
:meth:`pyspark.sql.functions.randn`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(0, 10, 1, 1).select(sf.randstr(16, 3)).show()
+----------------+
| randstr(16, 3)|
+----------------+
|nurJIpH4cmmMnsCG|
|fl9YtT5m01trZtIt|
|PD19rAgscTHS7qQZ|
|2CuAICF5UJOruVv4|
|kNZEs8nDpJEoz3Rl|
|OXiU0KN5eaXfjXFs|
|qfnTM1BZAHtN0gBV|
|1p8XiSKwg33KnRPK|
|od5y5MucayQq1bKK|
|tklYPmKmc5sIppWM|
+----------------+
"""
length = _enum_to_value(length)
length = lit(length)
if seed is None:
return _invoke_function_over_columns("randstr", length)
else:
seed = _enum_to_value(seed)
seed = lit(seed)
return _invoke_function_over_columns("randstr", length, seed)
@_try_remote_functions
def regexp_count(str: "ColumnOrName", regexp: "ColumnOrName") -> Column:
r"""Returns a count of the number of times that the Java regex pattern `regexp` is matched
in the string `str`.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
target column to work on.
regexp : :class:`~pyspark.sql.Column` or column name
regex pattern to apply.
Returns
-------
:class:`~pyspark.sql.Column`
the number of times that a Java regex pattern is matched in the string.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("1a 2b 14m", r"\d+")], ["str", "regexp"])
>>> df.select('*', sf.regexp_count('str', sf.lit(r'\d+'))).show()
+---------+------+----------------------+
| str|regexp|regexp_count(str, \d+)|
+---------+------+----------------------+
|1a 2b 14m| \d+| 3|
+---------+------+----------------------+
>>> df.select('*', sf.regexp_count('str', sf.lit(r'mmm'))).show()
+---------+------+----------------------+
| str|regexp|regexp_count(str, mmm)|
+---------+------+----------------------+
|1a 2b 14m| \d+| 0|
+---------+------+----------------------+
>>> df.select('*', sf.regexp_count("str", sf.col("regexp"))).show()
+---------+------+-------------------------+
| str|regexp|regexp_count(str, regexp)|
+---------+------+-------------------------+
|1a 2b 14m| \d+| 3|
+---------+------+-------------------------+
>>> df.select('*', sf.regexp_count(sf.col('str'), "regexp")).show()
+---------+------+-------------------------+
| str|regexp|regexp_count(str, regexp)|
+---------+------+-------------------------+
|1a 2b 14m| \d+| 3|
+---------+------+-------------------------+
"""
return _invoke_function_over_columns("regexp_count", str, regexp)
@_try_remote_functions
def regexp_extract(str: "ColumnOrName", pattern: str, idx: int) -> Column:
r"""Extract a specific group matched by the Java regex `regexp`, from the specified string column.
If the regex did not match, or the specified group did not match, an empty string is returned.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
target column to work on.
pattern : str
regex pattern to apply.
idx : int
matched group id.
Returns
-------
:class:`~pyspark.sql.Column`
matched value specified by `idx` group id.
See Also
--------
:meth:`pyspark.sql.functions.regexp_extract_all`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('100-200',)], ['str'])
>>> df.select('*', sf.regexp_extract('str', r'(\d+)-(\d+)', 1)).show()
+-------+-----------------------------------+
| str|regexp_extract(str, (\d+)-(\d+), 1)|
+-------+-----------------------------------+
|100-200| 100|
+-------+-----------------------------------+
>>> df = spark.createDataFrame([('foo',)], ['str'])
>>> df.select('*', sf.regexp_extract('str', r'(\d+)', 1)).show()
+---+-----------------------------+
|str|regexp_extract(str, (\d+), 1)|
+---+-----------------------------+
|foo| |
+---+-----------------------------+
>>> df = spark.createDataFrame([('aaaac',)], ['str'])
>>> df.select('*', sf.regexp_extract(sf.col('str'), '(a+)(b)?(c)', 2)).show()
+-----+-----------------------------------+
| str|regexp_extract(str, (a+)(b)?(c), 2)|
+-----+-----------------------------------+
|aaaac| |
+-----+-----------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"regexp_extract", _to_java_column(str), _enum_to_value(pattern), _enum_to_value(idx)
)
@_try_remote_functions
def regexp_extract_all(
str: "ColumnOrName", regexp: "ColumnOrName", idx: Optional[Union[int, Column]] = None
) -> Column:
r"""Extract all strings in the `str` that match the Java regex `regexp`
and corresponding to the regex group index.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
target column to work on.
regexp : :class:`~pyspark.sql.Column` or column name
regex pattern to apply.
idx : :class:`~pyspark.sql.Column` or int, optional
matched group id.
Returns
-------
:class:`~pyspark.sql.Column`
all strings in the `str` that match a Java regex and corresponding to the regex group index.
See Also
--------
:meth:`pyspark.sql.functions.regexp_extract`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("100-200, 300-400", r"(\d+)-(\d+)")], ["str", "regexp"])
>>> df.select('*', sf.regexp_extract_all('str', sf.lit(r'(\d+)-(\d+)'))).show()
+----------------+-----------+---------------------------------------+
| str| regexp|regexp_extract_all(str, (\d+)-(\d+), 1)|
+----------------+-----------+---------------------------------------+
|100-200, 300-400|(\d+)-(\d+)| [100, 300]|
+----------------+-----------+---------------------------------------+
>>> df.select('*', sf.regexp_extract_all('str', sf.lit(r'(\d+)-(\d+)'), sf.lit(1))).show()
+----------------+-----------+---------------------------------------+
| str| regexp|regexp_extract_all(str, (\d+)-(\d+), 1)|
+----------------+-----------+---------------------------------------+
|100-200, 300-400|(\d+)-(\d+)| [100, 300]|
+----------------+-----------+---------------------------------------+
>>> df.select('*', sf.regexp_extract_all('str', sf.lit(r'(\d+)-(\d+)'), 2)).show()
+----------------+-----------+---------------------------------------+
| str| regexp|regexp_extract_all(str, (\d+)-(\d+), 2)|
+----------------+-----------+---------------------------------------+
|100-200, 300-400|(\d+)-(\d+)| [200, 400]|
+----------------+-----------+---------------------------------------+
>>> df.select('*', sf.regexp_extract_all('str', sf.col("regexp"))).show()
+----------------+-----------+----------------------------------+
| str| regexp|regexp_extract_all(str, regexp, 1)|
+----------------+-----------+----------------------------------+
|100-200, 300-400|(\d+)-(\d+)| [100, 300]|
+----------------+-----------+----------------------------------+
>>> df.select('*', sf.regexp_extract_all(sf.col('str'), "regexp")).show()
+----------------+-----------+----------------------------------+
| str| regexp|regexp_extract_all(str, regexp, 1)|
+----------------+-----------+----------------------------------+
|100-200, 300-400|(\d+)-(\d+)| [100, 300]|
+----------------+-----------+----------------------------------+
"""
if idx is None:
return _invoke_function_over_columns("regexp_extract_all", str, regexp)
else:
return _invoke_function_over_columns("regexp_extract_all", str, regexp, lit(idx))
@_try_remote_functions
def regexp_replace(
string: "ColumnOrName", pattern: Union[str, Column], replacement: Union[str, Column]
) -> Column:
r"""Replace all substrings of the specified string value that match regexp with replacement.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
string : :class:`~pyspark.sql.Column` or str
column name or column containing the string value
pattern : :class:`~pyspark.sql.Column` or str
column object or str containing the regexp pattern
replacement : :class:`~pyspark.sql.Column` or str
column object or str containing the replacement
Returns
-------
:class:`~pyspark.sql.Column`
string with all substrings replaced.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("100-200", r"(\d+)", "--")],
... ["str", "pattern", "replacement"]
... )
Example 1: Replaces all the substrings in the `str` column name that
match the regex pattern `(\d+)` (one or more digits) with the replacement
string "--".
>>> df.select('*', sf.regexp_replace('str', r'(\d+)', '--')).show()
+-------+-------+-----------+---------------------------------+
| str|pattern|replacement|regexp_replace(str, (\d+), --, 1)|
+-------+-------+-----------+---------------------------------+
|100-200| (\d+)| --| -----|
+-------+-------+-----------+---------------------------------+
Example 2: Replaces all the substrings in the `str` Column that match
the regex pattern in the `pattern` Column with the string in the `replacement`
column.
>>> df.select('*', \
... sf.regexp_replace(sf.col("str"), sf.col("pattern"), sf.col("replacement")) \
... ).show()
+-------+-------+-----------+--------------------------------------------+
| str|pattern|replacement|regexp_replace(str, pattern, replacement, 1)|
+-------+-------+-----------+--------------------------------------------+
|100-200| (\d+)| --| -----|
+-------+-------+-----------+--------------------------------------------+
"""
return _invoke_function_over_columns("regexp_replace", string, lit(pattern), lit(replacement))
@_try_remote_functions
def regexp_substr(str: "ColumnOrName", regexp: "ColumnOrName") -> Column:
r"""Returns the first substring that matches the Java regex `regexp` within the string `str`.
If the regular expression is not found, the result is null.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
target column to work on.
regexp : :class:`~pyspark.sql.Column` or column name
regex pattern to apply.
Returns
-------
:class:`~pyspark.sql.Column`
the first substring that matches a Java regex within the string `str`.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("1a 2b 14m", r"\d+")], ["str", "regexp"])
Example 1: Returns the first substring in the `str` column name that
matches the regex pattern `(\d+)` (one or more digits).
>>> df.select('*', sf.regexp_substr('str', sf.lit(r'\d+'))).show()
+---------+------+-----------------------+
| str|regexp|regexp_substr(str, \d+)|
+---------+------+-----------------------+
|1a 2b 14m| \d+| 1|
+---------+------+-----------------------+
Example 2: Returns the first substring in the `str` column name that
matches the regex pattern `(mmm)` (three consecutive 'm' characters)
>>> df.select('*', sf.regexp_substr('str', sf.lit(r'mmm'))).show()
+---------+------+-----------------------+
| str|regexp|regexp_substr(str, mmm)|
+---------+------+-----------------------+
|1a 2b 14m| \d+| NULL|
+---------+------+-----------------------+
Example 3: Returns the first substring in the `str` column name that
matches the regex pattern in `regexp` Column.
>>> df.select('*', sf.regexp_substr("str", sf.col("regexp"))).show()
+---------+------+--------------------------+
| str|regexp|regexp_substr(str, regexp)|
+---------+------+--------------------------+
|1a 2b 14m| \d+| 1|
+---------+------+--------------------------+
Example 4: Returns the first substring in the `str` Column that
matches the regex pattern in `regexp` column name.
>>> df.select('*', sf.regexp_substr(sf.col("str"), "regexp")).show()
+---------+------+--------------------------+
| str|regexp|regexp_substr(str, regexp)|
+---------+------+--------------------------+
|1a 2b 14m| \d+| 1|
+---------+------+--------------------------+
"""
return _invoke_function_over_columns("regexp_substr", str, regexp)
@_try_remote_functions
def regexp_instr(
str: "ColumnOrName", regexp: "ColumnOrName", idx: Optional[Union[int, Column]] = None
) -> Column:
r"""Returns the position of the first substring in the `str` that match the Java regex `regexp`
and corresponding to the regex group index.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
target column to work on.
regexp : :class:`~pyspark.sql.Column` or column name
regex pattern to apply.
idx : :class:`~pyspark.sql.Column` or int, optional
matched group id.
Returns
-------
:class:`~pyspark.sql.Column`
the position of the first substring in the `str` that match a Java regex and corresponding
to the regex group index.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("1a 2b 14m", r"\d+(a|b|m)")], ["str", "regexp"])
Example 1: Returns the position of the first substring in the `str` column name that
match the regex pattern `(\d+(a|b|m))` (one or more digits followed by 'a', 'b', or 'm').
>>> df.select('*', sf.regexp_instr('str', sf.lit(r'\d+(a|b|m)'))).show()
+---------+----------+--------------------------------+
| str| regexp|regexp_instr(str, \d+(a|b|m), 0)|
+---------+----------+--------------------------------+
|1a 2b 14m|\d+(a|b|m)| 1|
+---------+----------+--------------------------------+
Example 2: Returns the position of the first substring in the `str` column name that
match the regex pattern `(\d+(a|b|m))` (one or more digits followed by 'a', 'b', or 'm'),
>>> df.select('*', sf.regexp_instr('str', sf.lit(r'\d+(a|b|m)'), sf.lit(1))).show()
+---------+----------+--------------------------------+
| str| regexp|regexp_instr(str, \d+(a|b|m), 1)|
+---------+----------+--------------------------------+
|1a 2b 14m|\d+(a|b|m)| 1|
+---------+----------+--------------------------------+
Example 3: Returns the position of the first substring in the `str` column name that
match the regex pattern in `regexp` Column.
>>> df.select('*', sf.regexp_instr('str', sf.col("regexp"))).show()
+---------+----------+----------------------------+
| str| regexp|regexp_instr(str, regexp, 0)|
+---------+----------+----------------------------+
|1a 2b 14m|\d+(a|b|m)| 1|
+---------+----------+----------------------------+
Example 4: Returns the position of the first substring in the `str` Column that
match the regex pattern in `regexp` column name.
>>> df.select('*', sf.regexp_instr(sf.col("str"), "regexp")).show()
+---------+----------+----------------------------+
| str| regexp|regexp_instr(str, regexp, 0)|
+---------+----------+----------------------------+
|1a 2b 14m|\d+(a|b|m)| 1|
+---------+----------+----------------------------+
"""
if idx is None:
return _invoke_function_over_columns("regexp_instr", str, regexp)
else:
return _invoke_function_over_columns("regexp_instr", str, regexp, lit(idx))
@_try_remote_functions
def initcap(col: "ColumnOrName") -> Column:
"""Translate the first letter of each word to upper case in the sentence.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
string with all first letters are uppercase in each word.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('ab cd',)], ['a'])
>>> df.select("*", sf.initcap("a")).show()
+-----+----------+
| a|initcap(a)|
+-----+----------+
|ab cd| Ab Cd|
+-----+----------+
"""
return _invoke_function_over_columns("initcap", col)
@_try_remote_functions
def soundex(col: "ColumnOrName") -> Column:
"""
Returns the SoundEx encoding for a string
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
SoundEx encoded string.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("Peters",),("Uhrbach",)], ["s"])
>>> df.select("*", sf.soundex("s")).show()
+-------+----------+
| s|soundex(s)|
+-------+----------+
| Peters| P362|
|Uhrbach| U612|
+-------+----------+
"""
return _invoke_function_over_columns("soundex", col)
@_try_remote_functions
def bin(col: "ColumnOrName") -> Column:
"""Returns the string representation of the binary value of the given column.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
binary representation of given value as string.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(10).select("*", sf.bin("id")).show()
+---+-------+
| id|bin(id)|
+---+-------+
| 0| 0|
| 1| 1|
| 2| 10|
| 3| 11|
| 4| 100|
| 5| 101|
| 6| 110|
| 7| 111|
| 8| 1000|
| 9| 1001|
+---+-------+
"""
return _invoke_function_over_columns("bin", col)
@_try_remote_functions
def hex(col: "ColumnOrName") -> Column:
"""Computes hex value of the given column, which could be :class:`pyspark.sql.types.StringType`,
:class:`pyspark.sql.types.BinaryType`, :class:`pyspark.sql.types.IntegerType` or
:class:`pyspark.sql.types.LongType`.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
See Also
--------
:meth:`pyspark.sql.functions.unhex`
Returns
-------
:class:`~pyspark.sql.Column`
hexadecimal representation of given value as string.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('ABC', 3)], ['a', 'b'])
>>> df.select('*', sf.hex('a'), sf.hex(df.b)).show()
+---+---+------+------+
| a| b|hex(a)|hex(b)|
+---+---+------+------+
|ABC| 3|414243| 3|
+---+---+------+------+
"""
return _invoke_function_over_columns("hex", col)
@_try_remote_functions
def unhex(col: "ColumnOrName") -> Column:
"""Inverse of hex. Interprets each pair of characters as a hexadecimal number
and converts to the byte representation of number.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
See Also
--------
:meth:`pyspark.sql.functions.hex`
Returns
-------
:class:`~pyspark.sql.Column`
string representation of given hexadecimal value.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('414243',)], ['a'])
>>> df.select('*', sf.unhex('a')).show()
+------+----------+
| a| unhex(a)|
+------+----------+
|414243|[41 42 43]|
+------+----------+
"""
return _invoke_function_over_columns("unhex", col)
@_try_remote_functions
def uniform(
min: Union[Column, int, float],
max: Union[Column, int, float],
seed: Optional[Union[Column, int]] = None,
) -> Column:
"""Returns a random value with independent and identically distributed (i.i.d.) values with the
specified range of numbers. The random seed is optional. The provided numbers specifying the
minimum and maximum values of the range must be constant. If both of these numbers are integers,
then the result will also be an integer. Otherwise if one or both of these are floating-point
numbers, then the result will also be a floating-point number.
.. versionadded:: 4.0.0
Parameters
----------
min : :class:`~pyspark.sql.Column`, int, or float
Minimum value in the range.
max : :class:`~pyspark.sql.Column`, int, or float
Maximum value in the range.
seed : :class:`~pyspark.sql.Column` or int
Optional random number seed to use.
Returns
-------
:class:`~pyspark.sql.Column`
The generated random number within the specified range.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(0, 10, 1, 1).select(sf.uniform(5, 105, 3)).show()
+------------------+
|uniform(5, 105, 3)|
+------------------+
| 30|
| 71|
| 99|
| 77|
| 16|
| 25|
| 89|
| 80|
| 51|
| 83|
+------------------+
"""
min = _enum_to_value(min)
min = lit(min)
max = _enum_to_value(max)
max = lit(max)
if seed is None:
return _invoke_function_over_columns("uniform", min, max)
else:
seed = _enum_to_value(seed)
seed = lit(seed)
return _invoke_function_over_columns("uniform", min, max, seed)
@_try_remote_functions
def length(col: "ColumnOrName") -> Column:
"""Computes the character length of string data or number of bytes of binary data.
The length of character data includes the trailing spaces. The length of binary data
includes binary zeros.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
length of the value.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.createDataFrame([('ABC ',)], ['a']).select('*', sf.length('a')).show()
+----+---------+
| a|length(a)|
+----+---------+
|ABC | 4|
+----+---------+
"""
return _invoke_function_over_columns("length", col)
@_try_remote_functions
def octet_length(col: "ColumnOrName") -> Column:
"""
Calculates the byte length for the specified string column.
.. versionadded:: 3.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Source column or strings
Returns
-------
:class:`~pyspark.sql.Column`
Byte length of the col
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('cat',), ( '\U0001F408',)], ['cat'])
>>> df.select('*', sf.octet_length('cat')).show()
+---+-----------------+
|cat|octet_length(cat)|
+---+-----------------+
|cat| 3|
| 🐈| 4|
+---+-----------------+
"""
return _invoke_function_over_columns("octet_length", col)
@_try_remote_functions
def bit_length(col: "ColumnOrName") -> Column:
"""
Calculates the bit length for the specified string column.
.. versionadded:: 3.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Source column or strings
Returns
-------
:class:`~pyspark.sql.Column`
Bit length of the col
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('cat',), ( '\U0001F408',)], ['cat'])
>>> df.select('*', sf.bit_length('cat')).show()
+---+---------------+
|cat|bit_length(cat)|
+---+---------------+
|cat| 24|
| 🐈| 32|
+---+---------------+
"""
return _invoke_function_over_columns("bit_length", col)
@_try_remote_functions
def translate(srcCol: "ColumnOrName", matching: str, replace: str) -> Column:
"""A function translate any character in the `srcCol` by a character in `matching`.
The characters in `replace` is corresponding to the characters in `matching`.
Translation will happen whenever any character in the string is matching with the character
in the `matching`.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
srcCol : :class:`~pyspark.sql.Column` or column name
Source column or strings
matching : str
matching characters.
replace : str
characters for replacement. If this is shorter than `matching` string then
those chars that don't have replacement will be dropped.
Returns
-------
:class:`~pyspark.sql.Column`
replaced value.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('translate',)], ['a'])
>>> df.select('*', sf.translate('a', "rnlt", "123")).show()
+---------+-----------------------+
| a|translate(a, rnlt, 123)|
+---------+-----------------------+
|translate| 1a2s3ae|
+---------+-----------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function(
"translate", _to_java_column(srcCol), _enum_to_value(matching), _enum_to_value(replace)
)
@_try_remote_functions
def to_binary(col: "ColumnOrName", format: Optional["ColumnOrName"] = None) -> Column:
"""
Converts the input `col` to a binary value based on the supplied `format`.
The `format` can be a case-insensitive string literal of "hex", "utf-8", "utf8",
or "base64". By default, the binary format for conversion is "hex" if
`format` is omitted. The function returns NULL if at least one of the
input parameters is NULL.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Input column or strings.
format : :class:`~pyspark.sql.Column` or str, optional
format to use to convert binary values.
Examples
--------
Example 1: Convert string to a binary with encoding specified
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("abc",)], ["e"])
>>> df.select(sf.try_to_binary(df.e, sf.lit("utf-8")).alias('r')).collect()
[Row(r=bytearray(b'abc'))]
Example 2: Convert string to a timestamp without encoding specified
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("414243",)], ["e"])
>>> df.select(sf.try_to_binary(df.e).alias('r')).collect()
[Row(r=bytearray(b'ABC'))]
"""
if format is not None:
return _invoke_function_over_columns("to_binary", col, format)
else:
return _invoke_function_over_columns("to_binary", col)
@_try_remote_functions
def to_char(col: "ColumnOrName", format: "ColumnOrName") -> Column:
"""
Convert `col` to a string based on the `format`.
Throws an exception if the conversion fails. The format can consist of the following
characters, case insensitive:
'0' or '9': Specifies an expected digit between 0 and 9. A sequence of 0 or 9 in the
format string matches a sequence of digits in the input value, generating a result
string of the same length as the corresponding sequence in the format string.
The result string is left-padded with zeros if the 0/9 sequence comprises more digits
than the matching part of the decimal value, starts with 0, and is before the decimal
point. Otherwise, it is padded with spaces.
'.' or 'D': Specifies the position of the decimal point (optional, only allowed once).
',' or 'G': Specifies the position of the grouping (thousands) separator (,).
There must be a 0 or 9 to the left and right of each grouping separator.
'$': Specifies the location of the $ currency sign. This character may only be specified once.
'S' or 'MI': Specifies the position of a '-' or '+' sign (optional, only allowed once at
the beginning or end of the format string). Note that 'S' prints '+' for positive
values but 'MI' prints a space.
'PR': Only allowed at the end of the format string; specifies that the result string
will be wrapped by angle brackets if the input value is negative.
If `col` is a datetime, `format` shall be a valid datetime pattern, see
<a href="https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html">Patterns</a>.
If `col` is a binary, it is converted to a string in one of the formats:
'base64': a base 64 string.
'hex': a string in the hexadecimal format.
'utf-8': the input binary is decoded to UTF-8 string.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Input column or strings.
format : :class:`~pyspark.sql.Column` or str, optional
format to use to convert char values.
Examples
--------
>>> df = spark.createDataFrame([(78.12,)], ["e"])
>>> df.select(to_char(df.e, lit("$99.99")).alias('r')).collect()
[Row(r='$78.12')]
"""
return _invoke_function_over_columns("to_char", col, format)
@_try_remote_functions
def to_varchar(col: "ColumnOrName", format: "ColumnOrName") -> Column:
"""
Convert `col` to a string based on the `format`.
Throws an exception if the conversion fails. The format can consist of the following
characters, case insensitive:
'0' or '9': Specifies an expected digit between 0 and 9. A sequence of 0 or 9 in the
format string matches a sequence of digits in the input value, generating a result
string of the same length as the corresponding sequence in the format string.
The result string is left-padded with zeros if the 0/9 sequence comprises more digits
than the matching part of the decimal value, starts with 0, and is before the decimal
point. Otherwise, it is padded with spaces.
'.' or 'D': Specifies the position of the decimal point (optional, only allowed once).
',' or 'G': Specifies the position of the grouping (thousands) separator (,).
There must be a 0 or 9 to the left and right of each grouping separator.
'$': Specifies the location of the $ currency sign. This character may only be specified once.
'S' or 'MI': Specifies the position of a '-' or '+' sign (optional, only allowed once at
the beginning or end of the format string). Note that 'S' prints '+' for positive
values but 'MI' prints a space.
'PR': Only allowed at the end of the format string; specifies that the result string
will be wrapped by angle brackets if the input value is negative.
If `col` is a datetime, `format` shall be a valid datetime pattern, see
<a href="https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html">Patterns</a>.
If `col` is a binary, it is converted to a string in one of the formats:
'base64': a base 64 string.
'hex': a string in the hexadecimal format.
'utf-8': the input binary is decoded to UTF-8 string.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Input column or strings.
format : :class:`~pyspark.sql.Column` or str, optional
format to use to convert char values.
Examples
--------
>>> df = spark.createDataFrame([(78.12,)], ["e"])
>>> df.select(to_varchar(df.e, lit("$99.99")).alias('r')).collect()
[Row(r='$78.12')]
"""
return _invoke_function_over_columns("to_varchar", col, format)
@_try_remote_functions
def to_number(col: "ColumnOrName", format: "ColumnOrName") -> Column:
"""
Convert string 'col' to a number based on the string format 'format'.
Throws an exception if the conversion fails. The format can consist of the following
characters, case insensitive:
'0' or '9': Specifies an expected digit between 0 and 9. A sequence of 0 or 9 in the
format string matches a sequence of digits in the input string. If the 0/9
sequence starts with 0 and is before the decimal point, it can only match a digit
sequence of the same size. Otherwise, if the sequence starts with 9 or is after
the decimal point, it can match a digit sequence that has the same or smaller size.
'.' or 'D': Specifies the position of the decimal point (optional, only allowed once).
',' or 'G': Specifies the position of the grouping (thousands) separator (,).
There must be a 0 or 9 to the left and right of each grouping separator.
'col' must match the grouping separator relevant for the size of the number.
'$': Specifies the location of the $ currency sign. This character may only be
specified once.
'S' or 'MI': Specifies the position of a '-' or '+' sign (optional, only allowed
once at the beginning or end of the format string). Note that 'S' allows '-'
but 'MI' does not.
'PR': Only allowed at the end of the format string; specifies that 'col' indicates a
negative number with wrapping angled brackets.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Input column or strings.
format : :class:`~pyspark.sql.Column` or str, optional
format to use to convert number values.
Examples
--------
>>> df = spark.createDataFrame([("$78.12",)], ["e"])
>>> df.select(to_number(df.e, lit("$99.99")).alias('r')).collect()
[Row(r=Decimal('78.12'))]
"""
return _invoke_function_over_columns("to_number", col, format)
@_try_remote_functions
def replace(
src: "ColumnOrName", search: "ColumnOrName", replace: Optional["ColumnOrName"] = None
) -> Column:
"""
Replaces all occurrences of `search` with `replace`.
.. versionadded:: 3.5.0
Parameters
----------
src : :class:`~pyspark.sql.Column` or str
A column of string to be replaced.
search : :class:`~pyspark.sql.Column` or str
A column of string, If `search` is not found in `str`, `str` is returned unchanged.
replace : :class:`~pyspark.sql.Column` or str, optional
A column of string, If `replace` is not specified or is an empty string,
nothing replaces the string that is removed from `str`.
Examples
--------
>>> df = spark.createDataFrame([("ABCabc", "abc", "DEF",)], ["a", "b", "c"])
>>> df.select(replace(df.a, df.b, df.c).alias('r')).collect()
[Row(r='ABCDEF')]
>>> df.select(replace(df.a, df.b).alias('r')).collect()
[Row(r='ABC')]
"""
if replace is not None:
return _invoke_function_over_columns("replace", src, search, replace)
else:
return _invoke_function_over_columns("replace", src, search)
@_try_remote_functions
def split_part(src: "ColumnOrName", delimiter: "ColumnOrName", partNum: "ColumnOrName") -> Column:
"""
Splits `str` by delimiter and return requested part of the split (1-based).
If any input is null, returns null. if `partNum` is out of range of split parts,
returns empty string. If `partNum` is 0, throws an error. If `partNum` is negative,
the parts are counted backward from the end of the string.
If the `delimiter` is an empty string, the `str` is not split.
.. versionadded:: 3.5.0
Parameters
----------
src : :class:`~pyspark.sql.Column` or column name
A column of string to be split.
delimiter : :class:`~pyspark.sql.Column` or column name
A column of string, the delimiter used for split.
partNum : :class:`~pyspark.sql.Column` or column name
A column of string, requested part of the split (1-based).
See Also
--------
:meth:`pyspark.sql.functions.sentences`
:meth:`pyspark.sql.functions.split`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("11.12.13", ".", 3,)], ["a", "b", "c"])
>>> df.select("*", sf.split_part("a", "b", "c")).show()
+--------+---+---+-------------------+
| a| b| c|split_part(a, b, c)|
+--------+---+---+-------------------+
|11.12.13| .| 3| 13|
+--------+---+---+-------------------+
>>> df.select("*", sf.split_part(df.a, df.b, sf.lit(-2))).show()
+--------+---+---+--------------------+
| a| b| c|split_part(a, b, -2)|
+--------+---+---+--------------------+
|11.12.13| .| 3| 12|
+--------+---+---+--------------------+
"""
return _invoke_function_over_columns("split_part", src, delimiter, partNum)
@_try_remote_functions
def substr(
str: "ColumnOrName", pos: "ColumnOrName", len: Optional["ColumnOrName"] = None
) -> Column:
"""
Returns the substring of `str` that starts at `pos` and is of length `len`,
or the slice of byte array that starts at `pos` and is of length `len`.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or column name
A column of string.
pos : :class:`~pyspark.sql.Column` or column name
A column of string, the substring of `str` that starts at `pos`.
len : :class:`~pyspark.sql.Column` or column name, optional
A column of string, the substring of `str` is of length `len`.
Returns
-------
:class:`~pyspark.sql.Column`
substring of given value.
See Also
--------
:meth:`pyspark.sql.functions.instr`
:meth:`pyspark.sql.functions.substring`
:meth:`pyspark.sql.functions.substring_index`
:meth:`pyspark.sql.Column.substr`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Spark SQL", 5, 1,)], ["a", "b", "c"])
>>> df.select("*", sf.substr("a", "b", "c")).show()
+---------+---+---+---------------+
| a| b| c|substr(a, b, c)|
+---------+---+---+---------------+
|Spark SQL| 5| 1| k|
+---------+---+---+---------------+
>>> df.select("*", sf.substr(df.a, df.b)).show()
+---------+---+---+------------------------+
| a| b| c|substr(a, b, 2147483647)|
+---------+---+---+------------------------+
|Spark SQL| 5| 1| k SQL|
+---------+---+---+------------------------+
"""
if len is not None:
return _invoke_function_over_columns("substr", str, pos, len)
else:
return _invoke_function_over_columns("substr", str, pos)
@_try_remote_functions
def try_parse_url(
url: "ColumnOrName", partToExtract: "ColumnOrName", key: Optional["ColumnOrName"] = None
) -> Column:
"""
This is a special version of `parse_url` that performs the same operation, but returns a
NULL value instead of raising an error if the parsing cannot be performed.
.. versionadded:: 4.0.0
Parameters
----------
url : :class:`~pyspark.sql.Column` or str
A column of strings, each representing a URL.
partToExtract : :class:`~pyspark.sql.Column` or str
A column of strings, each representing the part to extract from the URL.
key : :class:`~pyspark.sql.Column` or str, optional
A column of strings, each representing the key of a query parameter in the URL.
Returns
-------
:class:`~pyspark.sql.Column`
A new column of strings, each representing the value of the extracted part from the URL.
Examples
--------
Example 1: Extracting the query part from a URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("https://spark.apache.org/path?query=1", "QUERY")],
... ["url", "part"]
... )
>>> df.select(sf.try_parse_url(df.url, df.part)).show()
+------------------------+
|try_parse_url(url, part)|
+------------------------+
| query=1|
+------------------------+
Example 2: Extracting the value of a specific query parameter from a URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("https://spark.apache.org/path?query=1", "QUERY", "query")],
... ["url", "part", "key"]
... )
>>> df.select(sf.try_parse_url(df.url, df.part, df.key)).show()
+-----------------------------+
|try_parse_url(url, part, key)|
+-----------------------------+
| 1|
+-----------------------------+
Example 3: Extracting the protocol part from a URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("https://spark.apache.org/path?query=1", "PROTOCOL")],
... ["url", "part"]
... )
>>> df.select(sf.try_parse_url(df.url, df.part)).show()
+------------------------+
|try_parse_url(url, part)|
+------------------------+
| https|
+------------------------+
Example 4: Extracting the host part from a URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("https://spark.apache.org/path?query=1", "HOST")],
... ["url", "part"]
... )
>>> df.select(sf.try_parse_url(df.url, df.part)).show()
+------------------------+
|try_parse_url(url, part)|
+------------------------+
| spark.apache.org|
+------------------------+
Example 5: Extracting the path part from a URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("https://spark.apache.org/path?query=1", "PATH")],
... ["url", "part"]
... )
>>> df.select(sf.try_parse_url(df.url, df.part)).show()
+------------------------+
|try_parse_url(url, part)|
+------------------------+
| /path|
+------------------------+
Example 6: Invalid URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("inva lid://spark.apache.org/path?query=1", "QUERY", "query")],
... ["url", "part", "key"]
... )
>>> df.select(sf.try_parse_url(df.url, df.part, df.key)).show()
+-----------------------------+
|try_parse_url(url, part, key)|
+-----------------------------+
| NULL|
+-----------------------------+
"""
if key is not None:
return _invoke_function_over_columns("try_parse_url", url, partToExtract, key)
else:
return _invoke_function_over_columns("try_parse_url", url, partToExtract)
@_try_remote_functions
def parse_url(
url: "ColumnOrName", partToExtract: "ColumnOrName", key: Optional["ColumnOrName"] = None
) -> Column:
"""
URL function: Extracts a specified part from a URL. If a key is provided,
it returns the associated query parameter value.
.. versionadded:: 3.5.0
Parameters
----------
url : :class:`~pyspark.sql.Column` or str
A column of strings, each representing a URL.
partToExtract : :class:`~pyspark.sql.Column` or str
A column of strings, each representing the part to extract from the URL.
key : :class:`~pyspark.sql.Column` or str, optional
A column of strings, each representing the key of a query parameter in the URL.
Returns
-------
:class:`~pyspark.sql.Column`
A new column of strings, each representing the value of the extracted part from the URL.
Examples
--------
Example 1: Extracting the query part from a URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("https://spark.apache.org/path?query=1", "QUERY")],
... ["url", "part"]
... )
>>> df.select(sf.parse_url(df.url, df.part)).show()
+--------------------+
|parse_url(url, part)|
+--------------------+
| query=1|
+--------------------+
Example 2: Extracting the value of a specific query parameter from a URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("https://spark.apache.org/path?query=1", "QUERY", "query")],
... ["url", "part", "key"]
... )
>>> df.select(sf.parse_url(df.url, df.part, df.key)).show()
+-------------------------+
|parse_url(url, part, key)|
+-------------------------+
| 1|
+-------------------------+
Example 3: Extracting the protocol part from a URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("https://spark.apache.org/path?query=1", "PROTOCOL")],
... ["url", "part"]
... )
>>> df.select(sf.parse_url(df.url, df.part)).show()
+--------------------+
|parse_url(url, part)|
+--------------------+
| https|
+--------------------+
Example 4: Extracting the host part from a URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("https://spark.apache.org/path?query=1", "HOST")],
... ["url", "part"]
... )
>>> df.select(sf.parse_url(df.url, df.part)).show()
+--------------------+
|parse_url(url, part)|
+--------------------+
| spark.apache.org|
+--------------------+
Example 5: Extracting the path part from a URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("https://spark.apache.org/path?query=1", "PATH")],
... ["url", "part"]
... )
>>> df.select(sf.parse_url(df.url, df.part)).show()
+--------------------+
|parse_url(url, part)|
+--------------------+
| /path|
+--------------------+
"""
if key is not None:
return _invoke_function_over_columns("parse_url", url, partToExtract, key)
else:
return _invoke_function_over_columns("parse_url", url, partToExtract)
@_try_remote_functions
def printf(format: "ColumnOrName", *cols: "ColumnOrName") -> Column:
"""
Formats the arguments in printf-style and returns the result as a string column.
.. versionadded:: 3.5.0
Parameters
----------
format : :class:`~pyspark.sql.Column` or str
string that can contain embedded format tags and used as result column's value
cols : :class:`~pyspark.sql.Column` or str
column names or :class:`~pyspark.sql.Column`\\s to be used in formatting
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [("aa%d%s", 123, "cc",)], ["a", "b", "c"]
... ).select(sf.printf("a", "b", "c")).show()
+---------------+
|printf(a, b, c)|
+---------------+
| aa123cc|
+---------------+
"""
from pyspark.sql.classic.column import _to_seq, _to_java_column
sc = _get_active_spark_context()
return _invoke_function("printf", _to_java_column(format), _to_seq(sc, cols, _to_java_column))
@_try_remote_functions
def url_decode(str: "ColumnOrName") -> Column:
"""
URL function: Decodes a URL-encoded string in 'application/x-www-form-urlencoded'
format to its original format.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
A column of strings, each representing a URL-encoded string.
Returns
-------
:class:`~pyspark.sql.Column`
A new column of strings, each representing the decoded string.
Examples
--------
Example 1: Decoding a URL-encoded string
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("https%3A%2F%2Fspark.apache.org",)], ["url"])
>>> df.select(sf.url_decode(df.url)).show(truncate=False)
+------------------------+
|url_decode(url) |
+------------------------+
|https://spark.apache.org|
+------------------------+
Example 2: Decoding a URL-encoded string with spaces
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Hello%20World%21",)], ["url"])
>>> df.select(sf.url_decode(df.url)).show()
+---------------+
|url_decode(url)|
+---------------+
| Hello World!|
+---------------+
Example 3: Decoding a URL-encoded string with special characters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("A%2BB%3D%3D",)], ["url"])
>>> df.select(sf.url_decode(df.url)).show()
+---------------+
|url_decode(url)|
+---------------+
| A+B==|
+---------------+
Example 4: Decoding a URL-encoded string with non-ASCII characters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("%E4%BD%A0%E5%A5%BD",)], ["url"])
>>> df.select(sf.url_decode(df.url)).show()
+---------------+
|url_decode(url)|
+---------------+
| 你好|
+---------------+
Example 5: Decoding a URL-encoded string with hexadecimal values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("%7E%21%40%23%24%25%5E%26%2A%28%29%5F%2B",)], ["url"])
>>> df.select(sf.url_decode(df.url)).show()
+---------------+
|url_decode(url)|
+---------------+
| ~!@#$%^&*()_+|
+---------------+
"""
return _invoke_function_over_columns("url_decode", str)
@_try_remote_functions
def try_url_decode(str: "ColumnOrName") -> Column:
"""
This is a special version of `url_decode` that performs the same operation, but returns a
NULL value instead of raising an error if the decoding cannot be performed.
.. versionadded:: 4.0.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
A column of strings, each representing a URL-encoded string.
Returns
-------
:class:`~pyspark.sql.Column`
A new column of strings, each representing the decoded string.
Examples
--------
Example 1: Decoding a URL-encoded string
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("https%3A%2F%2Fspark.apache.org",)], ["url"])
>>> df.select(sf.try_url_decode(df.url)).show(truncate=False)
+------------------------+
|try_url_decode(url) |
+------------------------+
|https://spark.apache.org|
+------------------------+
Example 2: Return NULL if the decoding cannot be performed.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("https%3A%2F%2spark.apache.org",)], ["url"])
>>> df.select(sf.try_url_decode(df.url)).show()
+-------------------+
|try_url_decode(url)|
+-------------------+
| NULL|
+-------------------+
"""
return _invoke_function_over_columns("try_url_decode", str)
@_try_remote_functions
def url_encode(str: "ColumnOrName") -> Column:
"""
URL function: Encodes a string into a URL-encoded string in
'application/x-www-form-urlencoded' format.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
A column of strings, each representing a string to be URL-encoded.
Returns
-------
:class:`~pyspark.sql.Column`
A new column of strings, each representing the URL-encoded string.
Examples
--------
Example 1: Encoding a simple URL
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("https://spark.apache.org",)], ["url"])
>>> df.select(sf.url_encode(df.url)).show(truncate=False)
+------------------------------+
|url_encode(url) |
+------------------------------+
|https%3A%2F%2Fspark.apache.org|
+------------------------------+
Example 2: Encoding a URL with spaces
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Hello World!",)], ["url"])
>>> df.select(sf.url_encode(df.url)).show()
+---------------+
|url_encode(url)|
+---------------+
| Hello+World%21|
+---------------+
Example 3: Encoding a URL with special characters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("A+B==",)], ["url"])
>>> df.select(sf.url_encode(df.url)).show()
+---------------+
|url_encode(url)|
+---------------+
| A%2BB%3D%3D|
+---------------+
Example 4: Encoding a URL with non-ASCII characters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("你好",)], ["url"])
>>> df.select(sf.url_encode(df.url)).show()
+------------------+
| url_encode(url)|
+------------------+
|%E4%BD%A0%E5%A5%BD|
+------------------+
Example 5: Encoding a URL with hexadecimal values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("~!@#$%^&*()_+",)], ["url"])
>>> df.select(sf.url_encode(df.url)).show(truncate=False)
+-----------------------------------+
|url_encode(url) |
+-----------------------------------+
|%7E%21%40%23%24%25%5E%26*%28%29_%2B|
+-----------------------------------+
"""
return _invoke_function_over_columns("url_encode", str)
@_try_remote_functions
def position(
substr: "ColumnOrName", str: "ColumnOrName", start: Optional["ColumnOrName"] = None
) -> Column:
"""
Returns the position of the first occurrence of `substr` in `str` after position `start`.
The given `start` and return value are 1-based.
.. versionadded:: 3.5.0
Parameters
----------
substr : :class:`~pyspark.sql.Column` or str
A column of string, substring.
str : :class:`~pyspark.sql.Column` or str
A column of string.
start : :class:`~pyspark.sql.Column` or str, optional
A column of string, start position.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [("bar", "foobarbar", 5,)], ["a", "b", "c"]
... ).select(sf.position("a", "b", "c")).show()
+-----------------+
|position(a, b, c)|
+-----------------+
| 7|
+-----------------+
>>> spark.createDataFrame(
... [("bar", "foobarbar", 5,)], ["a", "b", "c"]
... ).select(sf.position("a", "b")).show()
+-----------------+
|position(a, b, 1)|
+-----------------+
| 4|
+-----------------+
"""
if start is not None:
return _invoke_function_over_columns("position", substr, str, start)
else:
return _invoke_function_over_columns("position", substr, str)
@_try_remote_functions
def endswith(str: "ColumnOrName", suffix: "ColumnOrName") -> Column:
"""
Returns a boolean. The value is True if str ends with suffix.
Returns NULL if either input expression is NULL. Otherwise, returns False.
Both str or suffix must be of STRING or BINARY type.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
A column of string.
suffix : :class:`~pyspark.sql.Column` or str
A column of string, the suffix.
Examples
--------
>>> df = spark.createDataFrame([("Spark SQL", "Spark",)], ["a", "b"])
>>> df.select(endswith(df.a, df.b).alias('r')).collect()
[Row(r=False)]
>>> df = spark.createDataFrame([("414243", "4243",)], ["e", "f"])
>>> df = df.select(to_binary("e").alias("e"), to_binary("f").alias("f"))
>>> df.printSchema()
root
|-- e: binary (nullable = true)
|-- f: binary (nullable = true)
>>> df.select(endswith("e", "f"), endswith("f", "e")).show()
+--------------+--------------+
|endswith(e, f)|endswith(f, e)|
+--------------+--------------+
| true| false|
+--------------+--------------+
"""
return _invoke_function_over_columns("endswith", str, suffix)
@_try_remote_functions
def startswith(str: "ColumnOrName", prefix: "ColumnOrName") -> Column:
"""
Returns a boolean. The value is True if str starts with prefix.
Returns NULL if either input expression is NULL. Otherwise, returns False.
Both str or prefix must be of STRING or BINARY type.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
A column of string.
prefix : :class:`~pyspark.sql.Column` or str
A column of string, the prefix.
Examples
--------
>>> df = spark.createDataFrame([("Spark SQL", "Spark",)], ["a", "b"])
>>> df.select(startswith(df.a, df.b).alias('r')).collect()
[Row(r=True)]
>>> df = spark.createDataFrame([("414243", "4142",)], ["e", "f"])
>>> df = df.select(to_binary("e").alias("e"), to_binary("f").alias("f"))
>>> df.printSchema()
root
|-- e: binary (nullable = true)
|-- f: binary (nullable = true)
>>> df.select(startswith("e", "f"), startswith("f", "e")).show()
+----------------+----------------+
|startswith(e, f)|startswith(f, e)|
+----------------+----------------+
| true| false|
+----------------+----------------+
"""
return _invoke_function_over_columns("startswith", str, prefix)
@_try_remote_functions
def char(col: "ColumnOrName") -> Column:
"""
Returns the ASCII character having the binary equivalent to `col`. If col is larger than 256 the
result is equivalent to char(col % 256)
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Input column or strings.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.char(sf.lit(65))).show()
+--------+
|char(65)|
+--------+
| A|
+--------+
"""
return _invoke_function_over_columns("char", col)
@_try_remote_functions
def btrim(str: "ColumnOrName", trim: Optional["ColumnOrName"] = None) -> Column:
"""
Remove the leading and trailing `trim` characters from `str`.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
Input column or strings.
trim : :class:`~pyspark.sql.Column` or str, optional
The trim string characters to trim, the default value is a single space
Examples
--------
>>> df = spark.createDataFrame([("SSparkSQLS", "SL", )], ['a', 'b'])
>>> df.select(btrim(df.a, df.b).alias('r')).collect()
[Row(r='parkSQ')]
>>> df = spark.createDataFrame([(" SparkSQL ",)], ['a'])
>>> df.select(btrim(df.a).alias('r')).collect()
[Row(r='SparkSQL')]
"""
if trim is not None:
return _invoke_function_over_columns("btrim", str, trim)
else:
return _invoke_function_over_columns("btrim", str)
@_try_remote_functions
def char_length(str: "ColumnOrName") -> Column:
"""
Returns the character length of string data or number of bytes of binary data.
The length of string data includes the trailing spaces.
The length of binary data includes binary zeros.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
Input column or strings.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.char_length(sf.lit("SparkSQL"))).show()
+---------------------+
|char_length(SparkSQL)|
+---------------------+
| 8|
+---------------------+
"""
return _invoke_function_over_columns("char_length", str)
@_try_remote_functions
def character_length(str: "ColumnOrName") -> Column:
"""
Returns the character length of string data or number of bytes of binary data.
The length of string data includes the trailing spaces.
The length of binary data includes binary zeros.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
Input column or strings.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.character_length(sf.lit("SparkSQL"))).show()
+--------------------------+
|character_length(SparkSQL)|
+--------------------------+
| 8|
+--------------------------+
"""
return _invoke_function_over_columns("character_length", str)
@_try_remote_functions
def chr(n: "ColumnOrName") -> Column:
"""
Returns the ASCII character having the binary equivalent to `n`.
If n is larger than 256 the result is equivalent to chr(n % 256).
.. versionadded:: 4.1.0
Parameters
----------
n : :class:`~pyspark.sql.Column` or column name
target column to compute on.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(60, 70).select("*", sf.chr("id")).show()
+---+-------+
| id|chr(id)|
+---+-------+
| 60| <|
| 61| =|
| 62| >|
| 63| ?|
| 64| @|
| 65| A|
| 66| B|
| 67| C|
| 68| D|
| 69| E|
+---+-------+
"""
return _invoke_function_over_columns("chr", n)
@_try_remote_functions
def try_to_binary(col: "ColumnOrName", format: Optional["ColumnOrName"] = None) -> Column:
"""
This is a special version of `to_binary` that performs the same operation, but returns a NULL
value instead of raising an error if the conversion cannot be performed.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Input column or strings.
format : :class:`~pyspark.sql.Column` or str, optional
format to use to convert binary values.
Examples
--------
Example 1: Convert string to a binary with encoding specified
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("abc",)], ["e"])
>>> df.select(sf.try_to_binary(df.e, sf.lit("utf-8")).alias('r')).collect()
[Row(r=bytearray(b'abc'))]
Example 2: Convert string to a timestamp without encoding specified
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("414243",)], ["e"])
>>> df.select(sf.try_to_binary(df.e).alias('r')).collect()
[Row(r=bytearray(b'ABC'))]
Example 3: Converion failure results in NULL when ANSI mode is on
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
... df = spark.range(1)
... df.select(sf.try_to_binary(sf.lit("malformed"), sf.lit("hex"))).show()
... finally:
... spark.conf.set("spark.sql.ansi.enabled", origin)
+-----------------------------+
|try_to_binary(malformed, hex)|
+-----------------------------+
| NULL|
+-----------------------------+
"""
if format is not None:
return _invoke_function_over_columns("try_to_binary", col, format)
else:
return _invoke_function_over_columns("try_to_binary", col)
@_try_remote_functions
def try_to_number(col: "ColumnOrName", format: "ColumnOrName") -> Column:
"""
Convert string 'col' to a number based on the string format `format`. Returns NULL if the
string 'col' does not match the expected format. The format follows the same semantics as the
to_number function.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Input column or strings.
format : :class:`~pyspark.sql.Column` or str, optional
format to use to convert number values.
Examples
--------
Example 1: Convert a string to a number with a format specified
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([("$78.12",)], ["e"])
>>> df.select(sf.try_to_number(df.e, sf.lit("$99.99")).alias('r')).show()
+-----+
| r|
+-----+
|78.12|
+-----+
Example 2: Converion failure results in NULL when ANSI mode is on
>>> import pyspark.sql.functions as sf
>>> origin = spark.conf.get("spark.sql.ansi.enabled")
>>> spark.conf.set("spark.sql.ansi.enabled", "true")
>>> try:
... df = spark.range(1)
... df.select(sf.try_to_number(sf.lit("77"), sf.lit("$99.99")).alias('r')).show()
... finally:
... spark.conf.set("spark.sql.ansi.enabled", origin)
+----+
| r|
+----+
|NULL|
+----+
"""
return _invoke_function_over_columns("try_to_number", col, format)
@_try_remote_functions
def contains(left: "ColumnOrName", right: "ColumnOrName") -> Column:
"""
Returns a boolean. The value is True if right is found inside left.
Returns NULL if either input expression is NULL. Otherwise, returns False.
Both left or right must be of STRING or BINARY type.
.. versionadded:: 3.5.0
Parameters
----------
left : :class:`~pyspark.sql.Column` or str
The input column or strings to check, may be NULL.
right : :class:`~pyspark.sql.Column` or str
The input column or strings to find, may be NULL.
Examples
--------
>>> df = spark.createDataFrame([("Spark SQL", "Spark")], ['a', 'b'])
>>> df.select(contains(df.a, df.b).alias('r')).collect()
[Row(r=True)]
>>> df = spark.createDataFrame([("414243", "4243",)], ["c", "d"])
>>> df = df.select(to_binary("c").alias("c"), to_binary("d").alias("d"))
>>> df.printSchema()
root
|-- c: binary (nullable = true)
|-- d: binary (nullable = true)
>>> df.select(contains("c", "d"), contains("d", "c")).show()
+--------------+--------------+
|contains(c, d)|contains(d, c)|
+--------------+--------------+
| true| false|
+--------------+--------------+
"""
return _invoke_function_over_columns("contains", left, right)
@_try_remote_functions
def elt(*inputs: "ColumnOrName") -> Column:
"""
Returns the `n`-th input, e.g., returns `input2` when `n` is 2.
The function returns NULL if the index exceeds the length of the array
and `spark.sql.ansi.enabled` is set to false. If `spark.sql.ansi.enabled` is set to true,
it throws ArrayIndexOutOfBoundsException for invalid indices.
.. versionadded:: 3.5.0
Parameters
----------
inputs : :class:`~pyspark.sql.Column` or str
Input columns or strings.
Examples
--------
>>> df = spark.createDataFrame([(1, "scala", "java")], ['a', 'b', 'c'])
>>> df.select(elt(df.a, df.b, df.c).alias('r')).collect()
[Row(r='scala')]
"""
from pyspark.sql.classic.column import _to_seq, _to_java_column
sc = _get_active_spark_context()
return _invoke_function("elt", _to_seq(sc, inputs, _to_java_column))
@_try_remote_functions
def find_in_set(str: "ColumnOrName", str_array: "ColumnOrName") -> Column:
"""
Returns the index (1-based) of the given string (`str`) in the comma-delimited
list (`strArray`). Returns 0, if the string was not found or if the given string (`str`)
contains a comma.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
The given string to be found.
str_array : :class:`~pyspark.sql.Column` or str
The comma-delimited list.
Examples
--------
>>> df = spark.createDataFrame([("ab", "abc,b,ab,c,def")], ['a', 'b'])
>>> df.select(find_in_set(df.a, df.b).alias('r')).collect()
[Row(r=3)]
"""
return _invoke_function_over_columns("find_in_set", str, str_array)
@_try_remote_functions
def like(
str: "ColumnOrName", pattern: "ColumnOrName", escapeChar: Optional["Column"] = None
) -> Column:
"""
Returns true if str matches `pattern` with `escape`,
null if any arguments are null, false otherwise.
The default escape character is the '\'.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
A string.
pattern : :class:`~pyspark.sql.Column` or str
A string. The pattern is a string which is matched literally, with
exception to the following special symbols:
_ matches any one character in the input (similar to . in posix regular expressions)
% matches zero or more characters in the input (similar to .* in posix regular
expressions)
Since Spark 2.0, string literals are unescaped in our SQL parser. For example, in order
to match "\abc", the pattern should be "\\abc".
When SQL config 'spark.sql.parser.escapedStringLiterals' is enabled, it falls back
to Spark 1.6 behavior regarding string literal parsing. For example, if the config is
enabled, the pattern to match "\abc" should be "\abc".
escapeChar : :class:`~pyspark.sql.Column`, optional
An character added since Spark 3.0. The default escape character is the '\'.
If an escape character precedes a special symbol or another escape character, the
following character is matched literally. It is invalid to escape any other character.
Examples
--------
>>> df = spark.createDataFrame([("Spark", "_park")], ['a', 'b'])
>>> df.select(like(df.a, df.b).alias('r')).collect()
[Row(r=True)]
>>> df = spark.createDataFrame(
... [("%SystemDrive%/Users/John", "/%SystemDrive/%//Users%")],
... ['a', 'b']
... )
>>> df.select(like(df.a, df.b, lit('/')).alias('r')).collect()
[Row(r=True)]
"""
if escapeChar is not None:
return _invoke_function_over_columns("like", str, pattern, escapeChar)
else:
return _invoke_function_over_columns("like", str, pattern)
@_try_remote_functions
def ilike(
str: "ColumnOrName", pattern: "ColumnOrName", escapeChar: Optional["Column"] = None
) -> Column:
"""
Returns true if str matches `pattern` with `escape` case-insensitively,
null if any arguments are null, false otherwise.
The default escape character is the '\'.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
A string.
pattern : :class:`~pyspark.sql.Column` or str
A string. The pattern is a string which is matched literally, with
exception to the following special symbols:
_ matches any one character in the input (similar to . in posix regular expressions)
% matches zero or more characters in the input (similar to .* in posix regular
expressions)
Since Spark 2.0, string literals are unescaped in our SQL parser. For example, in order
to match "\abc", the pattern should be "\\abc".
When SQL config 'spark.sql.parser.escapedStringLiterals' is enabled, it falls back
to Spark 1.6 behavior regarding string literal parsing. For example, if the config is
enabled, the pattern to match "\abc" should be "\abc".
escapeChar : :class:`~pyspark.sql.Column`, optional
An character added since Spark 3.0. The default escape character is the '\'.
If an escape character precedes a special symbol or another escape character, the
following character is matched literally. It is invalid to escape any other character.
Examples
--------
>>> df = spark.createDataFrame([("Spark", "_park")], ['a', 'b'])
>>> df.select(ilike(df.a, df.b).alias('r')).collect()
[Row(r=True)]
>>> df = spark.createDataFrame(
... [("%SystemDrive%/Users/John", "/%SystemDrive/%//Users%")],
... ['a', 'b']
... )
>>> df.select(ilike(df.a, df.b, lit('/')).alias('r')).collect()
[Row(r=True)]
"""
if escapeChar is not None:
return _invoke_function_over_columns("ilike", str, pattern, escapeChar)
else:
return _invoke_function_over_columns("ilike", str, pattern)
@_try_remote_functions
def lcase(str: "ColumnOrName") -> Column:
"""
Returns `str` with all characters changed to lowercase.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
Input column or strings.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.lcase(sf.lit("Spark"))).show()
+------------+
|lcase(Spark)|
+------------+
| spark|
+------------+
"""
return _invoke_function_over_columns("lcase", str)
@_try_remote_functions
def ucase(str: "ColumnOrName") -> Column:
"""
Returns `str` with all characters changed to uppercase.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
Input column or strings.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.ucase(sf.lit("Spark"))).show()
+------------+
|ucase(Spark)|
+------------+
| SPARK|
+------------+
"""
return _invoke_function_over_columns("ucase", str)
@_try_remote_functions
def left(str: "ColumnOrName", len: "ColumnOrName") -> Column:
"""
Returns the leftmost `len`(`len` can be string type) characters from the string `str`,
if `len` is less or equal than 0 the result is an empty string.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
Input column or strings.
len : :class:`~pyspark.sql.Column` or str
Input column or strings, the leftmost `len`.
Examples
--------
>>> df = spark.createDataFrame([("Spark SQL", 3,)], ['a', 'b'])
>>> df.select(left(df.a, df.b).alias('r')).collect()
[Row(r='Spa')]
"""
return _invoke_function_over_columns("left", str, len)
@_try_remote_functions
def right(str: "ColumnOrName", len: "ColumnOrName") -> Column:
"""
Returns the rightmost `len`(`len` can be string type) characters from the string `str`,
if `len` is less or equal than 0 the result is an empty string.
.. versionadded:: 3.5.0
Parameters
----------
str : :class:`~pyspark.sql.Column` or str
Input column or strings.
len : :class:`~pyspark.sql.Column` or str
Input column or strings, the rightmost `len`.
Examples
--------
>>> df = spark.createDataFrame([("Spark SQL", 3,)], ['a', 'b'])
>>> df.select(right(df.a, df.b).alias('r')).collect()
[Row(r='SQL')]
"""
return _invoke_function_over_columns("right", str, len)
@_try_remote_functions
def mask(
col: "ColumnOrName",
upperChar: Optional["ColumnOrName"] = None,
lowerChar: Optional["ColumnOrName"] = None,
digitChar: Optional["ColumnOrName"] = None,
otherChar: Optional["ColumnOrName"] = None,
) -> Column:
"""
Masks the given string value. This can be useful for creating copies of tables with sensitive
information removed.
.. versionadded:: 3.5.0
Parameters
----------
col: :class:`~pyspark.sql.Column` or str
target column to compute on.
upperChar: :class:`~pyspark.sql.Column` or str, optional
character to replace upper-case characters with. Specify NULL to retain original character.
lowerChar: :class:`~pyspark.sql.Column` or str, optional
character to replace lower-case characters with. Specify NULL to retain original character.
digitChar: :class:`~pyspark.sql.Column` or str, optional
character to replace digit characters with. Specify NULL to retain original character.
otherChar: :class:`~pyspark.sql.Column` or str, optional
character to replace all other characters with. Specify NULL to retain original character.
Returns
-------
:class:`~pyspark.sql.Column`
Examples
--------
>>> df = spark.createDataFrame([("AbCD123-@$#",), ("abcd-EFGH-8765-4321",)], ['data'])
>>> df.select(mask(df.data).alias('r')).collect()
[Row(r='XxXXnnn-@$#'), Row(r='xxxx-XXXX-nnnn-nnnn')]
>>> df.select(mask(df.data, lit('Y')).alias('r')).collect()
[Row(r='YxYYnnn-@$#'), Row(r='xxxx-YYYY-nnnn-nnnn')]
>>> df.select(mask(df.data, lit('Y'), lit('y')).alias('r')).collect()
[Row(r='YyYYnnn-@$#'), Row(r='yyyy-YYYY-nnnn-nnnn')]
>>> df.select(mask(df.data, lit('Y'), lit('y'), lit('d')).alias('r')).collect()
[Row(r='YyYYddd-@$#'), Row(r='yyyy-YYYY-dddd-dddd')]
>>> df.select(mask(df.data, lit('Y'), lit('y'), lit('d'), lit('*')).alias('r')).collect()
[Row(r='YyYYddd****'), Row(r='yyyy*YYYY*dddd*dddd')]
"""
_upperChar = lit("X") if upperChar is None else upperChar
_lowerChar = lit("x") if lowerChar is None else lowerChar
_digitChar = lit("n") if digitChar is None else digitChar
_otherChar = lit(None) if otherChar is None else otherChar
return _invoke_function_over_columns(
"mask", col, _upperChar, _lowerChar, _digitChar, _otherChar
)
@_try_remote_functions
def collate(col: "ColumnOrName", collation: str) -> Column:
"""
Marks a given column with specified collation.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Target string column to work on.
collation : str
Target collation name.
Returns
-------
:class:`~pyspark.sql.Column`
A new column of string type, where each value has the specified collation.
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("collate", _to_java_column(col), _enum_to_value(collation))
@_try_remote_functions
def collation(col: "ColumnOrName") -> Column:
"""
Returns the collation name of a given column.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Target string column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
collation name of a given expression.
Examples
--------
>>> df = spark.createDataFrame([('name',)], ['dt'])
>>> df.select(collation('dt').alias('collation')).show(truncate=False)
+--------------------------+
|collation |
+--------------------------+
|SYSTEM.BUILTIN.UTF8_BINARY|
+--------------------------+
"""
return _invoke_function_over_columns("collation", col)
@_try_remote_functions
def quote(col: "ColumnOrName") -> Column:
r"""Returns `str` enclosed by single quotes and each instance of
single quote in it is preceded by a backslash.
.. versionadded:: 4.1.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to be quoted.
Returns
-------
:class:`~pyspark.sql.Column`
quoted string
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(["Don't"], "STRING")
>>> df.select("*", sf.quote("value")).show()
+-----+------------+
|value|quote(value)|
+-----+------------+
|Don't| 'Don\'t'|
+-----+------------+
"""
return _invoke_function_over_columns("quote", col)
# ---------------------- Collection functions ------------------------------
@overload
def create_map(*cols: "ColumnOrName") -> Column:
...
@overload
def create_map(__cols: Union[Sequence["ColumnOrName"], Tuple["ColumnOrName", ...]]) -> Column:
...
@_try_remote_functions
def create_map(
*cols: Union["ColumnOrName", Union[Sequence["ColumnOrName"], Tuple["ColumnOrName", ...]]]
) -> Column:
"""
Map function: Creates a new map column from an even number of input columns or
column references. The input columns are grouped into key-value pairs to form a map.
For instance, the input (key1, value1, key2, value2, ...) would produce a map that
associates key1 with value1, key2 with value2, and so on. The function supports
grouping columns as a list as well.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : :class:`~pyspark.sql.Column` or str
The input column names or :class:`~pyspark.sql.Column` objects grouped into
key-value pairs. These can also be expressed as a list of columns.
Returns
-------
:class:`~pyspark.sql.Column`
A new Column of Map type, where each value is a map formed from the corresponding
key-value pairs provided in the input arguments.
Examples
--------
Example 1: Basic usage of create_map function.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], ("name", "age"))
>>> df.select(sf.create_map('name', 'age')).show()
+--------------+
|map(name, age)|
+--------------+
| {Alice -> 2}|
| {Bob -> 5}|
+--------------+
Example 2: Usage of create_map function with a list of columns.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], ("name", "age"))
>>> df.select(sf.create_map([df.name, df.age])).show()
+--------------+
|map(name, age)|
+--------------+
| {Alice -> 2}|
| {Bob -> 5}|
+--------------+
Example 3: Usage of create_map function with more than one key-value pair.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", 2, "female"),
... ("Bob", 5, "male")], ("name", "age", "gender"))
>>> df.select(sf.create_map(sf.lit('name'), df['name'],
... sf.lit('gender'), df['gender'])).show(truncate=False)
+---------------------------------+
|map(name, name, gender, gender) |
+---------------------------------+
|{name -> Alice, gender -> female}|
|{name -> Bob, gender -> male} |
+---------------------------------+
Example 4: Usage of create_map function with values of different types.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", 2, 22.2),
... ("Bob", 5, 36.1)], ("name", "age", "weight"))
>>> df.select(sf.create_map(sf.lit('age'), df['age'],
... sf.lit('weight'), df['weight'])).show(truncate=False)
+-----------------------------+
|map(age, age, weight, weight)|
+-----------------------------+
|{age -> 2.0, weight -> 22.2} |
|{age -> 5.0, weight -> 36.1} |
+-----------------------------+
"""
if len(cols) == 1 and isinstance(cols[0], (list, set)):
cols = cols[0] # type: ignore[assignment]
return _invoke_function_over_seq_of_columns("map", cols) # type: ignore[arg-type]
@_try_remote_functions
def map_from_arrays(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""
Map function: Creates a new map from two arrays. This function takes two arrays of
keys and values respectively, and returns a new map column.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or str
Name of column containing a set of keys. All elements should not be null.
col2 : :class:`~pyspark.sql.Column` or str
Name of column containing a set of values.
Returns
-------
:class:`~pyspark.sql.Column`
A column of map type.
Notes
-----
The input arrays for keys and values must have the same length and all elements
in keys should not be null. If these conditions are not met, an exception will be thrown.
Examples
--------
Example 1: Basic usage of map_from_arrays
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([2, 5], ['a', 'b'])], ['k', 'v'])
>>> df.select(sf.map_from_arrays(df.k, df.v)).show()
+---------------------+
|map_from_arrays(k, v)|
+---------------------+
| {2 -> a, 5 -> b}|
+---------------------+
Example 2: map_from_arrays with null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2], ['a', None])], ['k', 'v'])
>>> df.select(sf.map_from_arrays(df.k, df.v)).show()
+---------------------+
|map_from_arrays(k, v)|
+---------------------+
| {1 -> a, 2 -> NULL}|
+---------------------+
Example 3: map_from_arrays with empty arrays
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, StringType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField('k', ArrayType(IntegerType())),
... StructField('v', ArrayType(StringType()))
... ])
>>> df = spark.createDataFrame([([], [])], schema=schema)
>>> df.select(sf.map_from_arrays(df.k, df.v)).show()
+---------------------+
|map_from_arrays(k, v)|
+---------------------+
| {}|
+---------------------+
"""
return _invoke_function_over_columns("map_from_arrays", col1, col2)
@overload
def array(*cols: "ColumnOrName") -> Column:
...
@overload
def array(__cols: Union[Sequence["ColumnOrName"], Tuple["ColumnOrName", ...]]) -> Column:
...
@_try_remote_functions
def array(
*cols: Union["ColumnOrName", Union[Sequence["ColumnOrName"], Tuple["ColumnOrName", ...]]]
) -> Column:
"""
Collection function: Creates a new array column from the input columns or column names.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : :class:`~pyspark.sql.Column` or str
Column names or :class:`~pyspark.sql.Column` objects that have the same data type.
Returns
-------
:class:`~pyspark.sql.Column`
A new Column of array type, where each value is an array containing the corresponding values
from the input columns.
Examples
--------
Example 1: Basic usage of array function with column names.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", "doctor"), ("Bob", "engineer")],
... ("name", "occupation"))
>>> df.select(sf.array('name', 'occupation')).show()
+-----------------------+
|array(name, occupation)|
+-----------------------+
| [Alice, doctor]|
| [Bob, engineer]|
+-----------------------+
Example 2: Usage of array function with Column objects.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", "doctor"), ("Bob", "engineer")],
... ("name", "occupation"))
>>> df.select(sf.array(df.name, df.occupation)).show()
+-----------------------+
|array(name, occupation)|
+-----------------------+
| [Alice, doctor]|
| [Bob, engineer]|
+-----------------------+
Example 3: Single argument as list of column names.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", "doctor"), ("Bob", "engineer")],
... ("name", "occupation"))
>>> df.select(sf.array(['name', 'occupation'])).show()
+-----------------------+
|array(name, occupation)|
+-----------------------+
| [Alice, doctor]|
| [Bob, engineer]|
+-----------------------+
Example 4: Usage of array function with columns of different types.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [("Alice", 2, 22.2), ("Bob", 5, 36.1)],
... ("name", "age", "weight"))
>>> df.select(sf.array(['age', 'weight'])).show()
+------------------+
|array(age, weight)|
+------------------+
| [2.0, 22.2]|
| [5.0, 36.1]|
+------------------+
Example 5: array function with a column containing null values.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("Alice", None), ("Bob", "engineer")],
... ("name", "occupation"))
>>> df.select(sf.array('name', 'occupation')).show()
+-----------------------+
|array(name, occupation)|
+-----------------------+
| [Alice, NULL]|
| [Bob, engineer]|
+-----------------------+
"""
if len(cols) == 1 and isinstance(cols[0], (list, set)):
cols = cols[0] # type: ignore[assignment]
return _invoke_function_over_seq_of_columns("array", cols) # type: ignore[arg-type]
@_try_remote_functions
def array_contains(col: "ColumnOrName", value: Any) -> Column:
"""
Collection function: This function returns a boolean indicating whether the array
contains the given value, returning null if the array is null, true if the array
contains the given value, and false otherwise.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The target column containing the arrays.
value :
The value or column to check for in the array.
Returns
-------
:class:`~pyspark.sql.Column`
A new Column of Boolean type, where each value indicates whether the corresponding array
from the input column contains the specified value.
Examples
--------
Example 1: Basic usage of array_contains function.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
>>> df.select(sf.array_contains(df.data, "a")).show()
+-----------------------+
|array_contains(data, a)|
+-----------------------+
| true|
| false|
+-----------------------+
Example 2: Usage of array_contains function with a column.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"], "c"),
... (["c", "d", "e"], "d"),
... (["e", "a", "c"], "b")], ["data", "item"])
>>> df.select(sf.array_contains(df.data, sf.col("item"))).show()
+--------------------------+
|array_contains(data, item)|
+--------------------------+
| true|
| true|
| false|
+--------------------------+
Example 3: Attempt to use array_contains function with a null array.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(None,), (["a", "b", "c"],)], ['data'])
>>> df.select(sf.array_contains(df.data, "a")).show()
+-----------------------+
|array_contains(data, a)|
+-----------------------+
| NULL|
| true|
+-----------------------+
Example 4: Usage of array_contains with an array column containing null values.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", None, "c"],)], ['data'])
>>> df.select(sf.array_contains(df.data, "a")).show()
+-----------------------+
|array_contains(data, a)|
+-----------------------+
| true|
+-----------------------+
"""
return _invoke_function_over_columns("array_contains", col, lit(value))
@_try_remote_functions
def arrays_overlap(a1: "ColumnOrName", a2: "ColumnOrName") -> Column:
"""
Collection function: This function returns a boolean column indicating if the input arrays
have common non-null elements, returning true if they do, null if the arrays do not contain
any common elements but are not empty and at least one of them contains a null element,
and false otherwise.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
a1, a2 : :class:`~pyspark.sql.Column` or str
The names of the columns that contain the input arrays.
Returns
-------
:class:`~pyspark.sql.Column`
A new Column of Boolean type, where each value indicates whether the corresponding arrays
from the input columns contain any common elements.
Examples
--------
Example 1: Basic usage of arrays_overlap function.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b"], ["b", "c"]), (["a"], ["b", "c"])], ['x', 'y'])
>>> df.select(sf.arrays_overlap(df.x, df.y)).show()
+--------------------+
|arrays_overlap(x, y)|
+--------------------+
| true|
| false|
+--------------------+
Example 2: Usage of arrays_overlap function with arrays containing null elements.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", None], ["b", None]), (["a"], ["b", "c"])], ['x', 'y'])
>>> df.select(sf.arrays_overlap(df.x, df.y)).show()
+--------------------+
|arrays_overlap(x, y)|
+--------------------+
| NULL|
| false|
+--------------------+
Example 3: Usage of arrays_overlap function with arrays that are null.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(None, ["b", "c"]), (["a"], None)], ['x', 'y'])
>>> df.select(sf.arrays_overlap(df.x, df.y)).show()
+--------------------+
|arrays_overlap(x, y)|
+--------------------+
| NULL|
| NULL|
+--------------------+
Example 4: Usage of arrays_overlap on arrays with identical elements.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b"], ["a", "b"]), (["a"], ["a"])], ['x', 'y'])
>>> df.select(sf.arrays_overlap(df.x, df.y)).show()
+--------------------+
|arrays_overlap(x, y)|
+--------------------+
| true|
| true|
+--------------------+
"""
return _invoke_function_over_columns("arrays_overlap", a1, a2)
@_try_remote_functions
def slice(
x: "ColumnOrName", start: Union["ColumnOrName", int], length: Union["ColumnOrName", int]
) -> Column:
"""
Array function: Returns a new array column by slicing the input array column from
a start index to a specific length. The indices start at 1, and can be negative to index
from the end of the array. The length specifies the number of elements in the resulting array.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
x : :class:`~pyspark.sql.Column` or str
Input array column or column name to be sliced.
start : :class:`~pyspark.sql.Column`, str, or int
The start index for the slice operation. If negative, starts the index from the
end of the array.
length : :class:`~pyspark.sql.Column`, str, or int
The length of the slice, representing number of elements in the resulting array.
Returns
-------
:class:`~pyspark.sql.Column`
A new Column object of Array type, where each value is a slice of the corresponding
list from the input column.
Examples
--------
Example 1: Basic usage of the slice function.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3],), ([4, 5],)], ['x'])
>>> df.select(sf.slice(df.x, 2, 2)).show()
+--------------+
|slice(x, 2, 2)|
+--------------+
| [2, 3]|
| [5]|
+--------------+
Example 2: Slicing with negative start index.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3],), ([4, 5],)], ['x'])
>>> df.select(sf.slice(df.x, -1, 1)).show()
+---------------+
|slice(x, -1, 1)|
+---------------+
| [3]|
| [5]|
+---------------+
Example 3: Slice function with column inputs for start and length.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3], 2, 2), ([4, 5], 1, 3)], ['x', 'start', 'length'])
>>> df.select(sf.slice(df.x, df.start, df.length)).show()
+-----------------------+
|slice(x, start, length)|
+-----------------------+
| [2, 3]|
| [4, 5]|
+-----------------------+
"""
start = _enum_to_value(start)
start = lit(start) if isinstance(start, int) else start
length = _enum_to_value(length)
length = lit(length) if isinstance(length, int) else length
return _invoke_function_over_columns("slice", x, start, length)
@_try_remote_functions
def array_join(
col: "ColumnOrName", delimiter: str, null_replacement: Optional[str] = None
) -> Column:
"""
Array function: Returns a string column by concatenating the elements of the input
array column using the delimiter. Null values within the array can be replaced with
a specified string through the null_replacement argument. If null_replacement is
not set, null values are ignored.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The input column containing the arrays to be joined.
delimiter : str
The string to be used as the delimiter when joining the array elements.
null_replacement : str, optional
The string to replace null values within the array. If not set, null values are ignored.
Returns
-------
:class:`~pyspark.sql.Column`
A new column of string type, where each value is the result of joining the corresponding
array from the input column.
Examples
--------
Example 1: Basic usage of array_join function.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"],), (["a", "b"],)], ['data'])
>>> df.select(sf.array_join(df.data, ",")).show()
+-------------------+
|array_join(data, ,)|
+-------------------+
| a,b,c|
| a,b|
+-------------------+
Example 2: Usage of array_join function with null_replacement argument.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", None, "c"],)], ['data'])
>>> df.select(sf.array_join(df.data, ",", "NULL")).show()
+-------------------------+
|array_join(data, ,, NULL)|
+-------------------------+
| a,NULL,c|
+-------------------------+
Example 3: Usage of array_join function without null_replacement argument.
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", None, "c"],)], ['data'])
>>> df.select(sf.array_join(df.data, ",")).show()
+-------------------+
|array_join(data, ,)|
+-------------------+
| a,c|
+-------------------+
Example 4: Usage of array_join function with an array that is null.
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import StructType, StructField, ArrayType, StringType
>>> schema = StructType([StructField("data", ArrayType(StringType()), True)])
>>> df = spark.createDataFrame([(None,)], schema)
>>> df.select(sf.array_join(df.data, ",")).show()
+-------------------+
|array_join(data, ,)|
+-------------------+
| NULL|
+-------------------+
Example 5: Usage of array_join function with an array containing only null values.
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import StructType, StructField, ArrayType, StringType
>>> schema = StructType([StructField("data", ArrayType(StringType()), True)])
>>> df = spark.createDataFrame([([None, None],)], schema)
>>> df.select(sf.array_join(df.data, ",", "NULL")).show()
+-------------------------+
|array_join(data, ,, NULL)|
+-------------------------+
| NULL,NULL|
+-------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
_get_active_spark_context()
if null_replacement is None:
return _invoke_function("array_join", _to_java_column(col), _enum_to_value(delimiter))
else:
return _invoke_function(
"array_join",
_to_java_column(col),
_enum_to_value(delimiter),
_enum_to_value(null_replacement),
)
@_try_remote_functions
def concat(*cols: "ColumnOrName") -> Column:
"""
Collection function: Concatenates multiple input columns together into a single column.
The function works with strings, numeric, binary and compatible array columns.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : :class:`~pyspark.sql.Column` or str
target column or columns to work on.
Returns
-------
:class:`~pyspark.sql.Column`
concatenated values. Type of the `Column` depends on input columns' type.
See Also
--------
:meth:`pyspark.sql.functions.concat_ws`
:meth:`pyspark.sql.functions.array_join` : to concatenate string columns with delimiter
Examples
--------
Example 1: Concatenating string columns
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('abcd','123')], ['s', 'd'])
>>> df.select(sf.concat(df.s, df.d)).show()
+------------+
|concat(s, d)|
+------------+
| abcd123|
+------------+
Example 2: Concatenating array columns
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2], [3, 4], [5]), ([1, 2], None, [3])], ['a', 'b', 'c'])
>>> df.select(sf.concat(df.a, df.b, df.c)).show()
+---------------+
|concat(a, b, c)|
+---------------+
|[1, 2, 3, 4, 5]|
| NULL|
+---------------+
Example 3: Concatenating numeric columns
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, 2, 3)], ['a', 'b', 'c'])
>>> df.select(sf.concat(df.a, df.b, df.c)).show()
+---------------+
|concat(a, b, c)|
+---------------+
| 123|
+---------------+
Example 4: Concatenating binary columns
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(bytearray(b'abc'), bytearray(b'def'))], ['a', 'b'])
>>> df.select(sf.concat(df.a, df.b)).show()
+-------------------+
| concat(a, b)|
+-------------------+
|[61 62 63 64 65 66]|
+-------------------+
Example 5: Concatenating mixed types of columns
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,"abc",3,"def")], ['a','b','c','d'])
>>> df.select(sf.concat(df.a, df.b, df.c, df.d)).show()
+------------------+
|concat(a, b, c, d)|
+------------------+
| 1abc3def|
+------------------+
"""
return _invoke_function_over_seq_of_columns("concat", cols)
@_try_remote_functions
def array_position(col: "ColumnOrName", value: Any) -> Column:
"""
Array function: Locates the position of the first occurrence of the given value
in the given array. Returns null if either of the arguments are null.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Notes
-----
The position is not zero based, but 1 based index. Returns 0 if the given
value could not be found in the array.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
target column to work on.
value : Any
value or a :class:`~pyspark.sql.Column` expression to look for.
.. versionchanged:: 4.0.0
`value` now also accepts a Column type.
Returns
-------
:class:`~pyspark.sql.Column`
position of the value in the given array if found and 0 otherwise.
Examples
--------
Example 1: Finding the position of a string in an array of strings
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["c", "b", "a"],)], ['data'])
>>> df.select(sf.array_position(df.data, "a")).show()
+-----------------------+
|array_position(data, a)|
+-----------------------+
| 3|
+-----------------------+
Example 2: Finding the position of a string in an empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, StringType, StructField, StructType
>>> schema = StructType([StructField("data", ArrayType(StringType()), True)])
>>> df = spark.createDataFrame([([],)], schema=schema)
>>> df.select(sf.array_position(df.data, "a")).show()
+-----------------------+
|array_position(data, a)|
+-----------------------+
| 0|
+-----------------------+
Example 3: Finding the position of an integer in an array of integers
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3],)], ['data'])
>>> df.select(sf.array_position(df.data, 2)).show()
+-----------------------+
|array_position(data, 2)|
+-----------------------+
| 2|
+-----------------------+
Example 4: Finding the position of a non-existing value in an array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["c", "b", "a"],)], ['data'])
>>> df.select(sf.array_position(df.data, "d")).show()
+-----------------------+
|array_position(data, d)|
+-----------------------+
| 0|
+-----------------------+
Example 5: Finding the position of a value in an array with nulls
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([None, "b", "a"],)], ['data'])
>>> df.select(sf.array_position(df.data, "a")).show()
+-----------------------+
|array_position(data, a)|
+-----------------------+
| 3|
+-----------------------+
Example 6: Finding the position of a column's value in an array of integers
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([10, 20, 30], 20)], ['data', 'col'])
>>> df.select(sf.array_position(df.data, df.col)).show()
+-------------------------+
|array_position(data, col)|
+-------------------------+
| 2|
+-------------------------+
"""
return _invoke_function_over_columns("array_position", col, lit(value))
@_try_remote_functions
def element_at(col: "ColumnOrName", extraction: Any) -> Column:
"""
Collection function:
(array, index) - Returns element of array at given (1-based) index. If Index is 0, Spark will
throw an error. If index < 0, accesses elements from the last to the first.
If 'spark.sql.ansi.enabled' is set to true, an exception will be thrown if the index is out
of array boundaries instead of returning NULL.
(map, key) - Returns value for given key in `extraction` if col is map. The function always
returns NULL if the key is not contained in the map.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column containing array or map
extraction :
index to check for in array or key to check for in map
Returns
-------
:class:`~pyspark.sql.Column`
value at given position.
Notes
-----
The position is not zero based, but 1 based index.
If extraction is a string, :meth:`element_at` treats it as a literal string,
while :meth:`try_element_at` treats it as a column name.
See Also
--------
:meth:`pyspark.sql.functions.get`
:meth:`pyspark.sql.functions.try_element_at`
Examples
--------
Example 1: Getting the first element of an array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"],)], ['data'])
>>> df.select(sf.element_at(df.data, 1)).show()
+-------------------+
|element_at(data, 1)|
+-------------------+
| a|
+-------------------+
Example 2: Getting the last element of an array using negative index
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"],)], ['data'])
>>> df.select(sf.element_at(df.data, -1)).show()
+--------------------+
|element_at(data, -1)|
+--------------------+
| c|
+--------------------+
Example 3: Getting a value from a map using a key
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([({"a": 1.0, "b": 2.0},)], ['data'])
>>> df.select(sf.element_at(df.data, sf.lit("a"))).show()
+-------------------+
|element_at(data, a)|
+-------------------+
| 1.0|
+-------------------+
Example 4: Getting a non-existing value from a map using a key
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([({"a": 1.0, "b": 2.0},)], ['data'])
>>> df.select(sf.element_at(df.data, sf.lit("c"))).show()
+-------------------+
|element_at(data, c)|
+-------------------+
| NULL|
+-------------------+
Example 5: Getting a value from a map using a literal string as the key
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([({"a": 1.0, "b": 2.0}, "a")], ['data', 'b'])
>>> df.select(sf.element_at(df.data, 'b')).show()
+-------------------+
|element_at(data, b)|
+-------------------+
| 2.0|
+-------------------+
"""
return _invoke_function_over_columns("element_at", col, lit(extraction))
@_try_remote_functions
def try_element_at(col: "ColumnOrName", extraction: "ColumnOrName") -> Column:
"""
Collection function:
(array, index) - Returns element of array at given (1-based) index. If Index is 0, Spark will
throw an error. If index < 0, accesses elements from the last to the first. The function
always returns NULL if the index exceeds the length of the array.
(map, key) - Returns value for given key. The function always returns NULL if the key is not
contained in the map.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column containing array or map
extraction :
index to check for in array or key to check for in map
Notes
-----
The position is not zero based, but 1 based index.
If extraction is a string, :meth:`try_element_at` treats it as a column name,
while :meth:`element_at` treats it as a literal string.
See Also
--------
:meth:`pyspark.sql.functions.get`
:meth:`pyspark.sql.functions.element_at`
Examples
--------
Example 1: Getting the first element of an array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"],)], ['data'])
>>> df.select(sf.try_element_at(df.data, sf.lit(1))).show()
+-----------------------+
|try_element_at(data, 1)|
+-----------------------+
| a|
+-----------------------+
Example 2: Getting the last element of an array using negative index
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"],)], ['data'])
>>> df.select(sf.try_element_at(df.data, sf.lit(-1))).show()
+------------------------+
|try_element_at(data, -1)|
+------------------------+
| c|
+------------------------+
Example 3: Getting a value from a map using a key
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([({"a": 1.0, "b": 2.0},)], ['data'])
>>> df.select(sf.try_element_at(df.data, sf.lit("a"))).show()
+-----------------------+
|try_element_at(data, a)|
+-----------------------+
| 1.0|
+-----------------------+
Example 4: Getting a non-existing element from an array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"],)], ['data'])
>>> df.select(sf.try_element_at(df.data, sf.lit(4))).show()
+-----------------------+
|try_element_at(data, 4)|
+-----------------------+
| NULL|
+-----------------------+
Example 5: Getting a non-existing value from a map using a key
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([({"a": 1.0, "b": 2.0},)], ['data'])
>>> df.select(sf.try_element_at(df.data, sf.lit("c"))).show()
+-----------------------+
|try_element_at(data, c)|
+-----------------------+
| NULL|
+-----------------------+
Example 6: Getting a value from a map using a column name as the key
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([({"a": 1.0, "b": 2.0}, "a")], ['data', 'b'])
>>> df.select(sf.try_element_at(df.data, 'b')).show()
+-----------------------+
|try_element_at(data, b)|
+-----------------------+
| 1.0|
+-----------------------+
"""
return _invoke_function_over_columns("try_element_at", col, extraction)
@_try_remote_functions
def get(col: "ColumnOrName", index: Union["ColumnOrName", int]) -> Column:
"""
Array function: Returns the element of an array at the given (0-based) index.
If the index points outside of the array boundaries, then this function
returns NULL.
.. versionadded:: 3.4.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Name of the column containing the array.
index : :class:`~pyspark.sql.Column` or str or int
Index to check for in the array.
Returns
-------
:class:`~pyspark.sql.Column`
Value at the given position.
Notes
-----
The position is not 1-based, but 0-based index.
Supports Spark Connect.
See Also
--------
:meth:`pyspark.sql.functions.element_at`
Examples
--------
Example 1: Getting an element at a fixed position
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"],)], ['data'])
>>> df.select(sf.get(df.data, 1)).show()
+------------+
|get(data, 1)|
+------------+
| b|
+------------+
Example 2: Getting an element at a position outside the array boundaries
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"],)], ['data'])
>>> df.select(sf.get(df.data, 3)).show()
+------------+
|get(data, 3)|
+------------+
| NULL|
+------------+
Example 3: Getting an element at a position specified by another column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"], 2)], ['data', 'index'])
>>> df.select(sf.get(df.data, df.index)).show()
+----------------+
|get(data, index)|
+----------------+
| c|
+----------------+
Example 4: Getting an element at a position calculated from another column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"], 2)], ['data', 'index'])
>>> df.select(sf.get(df.data, df.index - 1)).show()
+----------------------+
|get(data, (index - 1))|
+----------------------+
| b|
+----------------------+
Example 5: Getting an element at a negative position
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(["a", "b", "c"], )], ['data'])
>>> df.select(sf.get(df.data, -1)).show()
+-------------+
|get(data, -1)|
+-------------+
| NULL|
+-------------+
"""
index = _enum_to_value(index)
index = lit(index) if isinstance(index, int) else index
return _invoke_function_over_columns("get", col, index)
@_try_remote_functions
def array_prepend(col: "ColumnOrName", value: Any) -> Column:
"""
Array function: Returns an array containing the given element as
the first element and the rest of the elements from the original array.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column containing array
value :
a literal value, or a :class:`~pyspark.sql.Column` expression.
Returns
-------
:class:`~pyspark.sql.Column`
an array with the given value prepended.
Examples
--------
Example 1: Prepending a column value to an array column
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2="c")])
>>> df.select(sf.array_prepend(df.c1, df.c2)).show()
+---------------------+
|array_prepend(c1, c2)|
+---------------------+
| [c, b, a, c]|
+---------------------+
Example 2: Prepending a numeric value to an array column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3],)], ['data'])
>>> df.select(sf.array_prepend(df.data, 4)).show()
+----------------------+
|array_prepend(data, 4)|
+----------------------+
| [4, 1, 2, 3]|
+----------------------+
Example 3: Prepending a null value to an array column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3],)], ['data'])
>>> df.select(sf.array_prepend(df.data, None)).show()
+-------------------------+
|array_prepend(data, NULL)|
+-------------------------+
| [NULL, 1, 2, 3]|
+-------------------------+
Example 4: Prepending a value to a NULL array column
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", ArrayType(IntegerType()), True)
... ])
>>> df = spark.createDataFrame([(None,)], schema=schema)
>>> df.select(sf.array_prepend(df.data, 4)).show()
+----------------------+
|array_prepend(data, 4)|
+----------------------+
| NULL|
+----------------------+
Example 5: Prepending a value to an empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", ArrayType(IntegerType()), True)
... ])
>>> df = spark.createDataFrame([([],)], schema=schema)
>>> df.select(sf.array_prepend(df.data, 1)).show()
+----------------------+
|array_prepend(data, 1)|
+----------------------+
| [1]|
+----------------------+
"""
return _invoke_function_over_columns("array_prepend", col, lit(value))
@_try_remote_functions
def array_remove(col: "ColumnOrName", element: Any) -> Column:
"""
Array function: Remove all elements that equal to element from the given array.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column containing array
element :
element or a :class:`~pyspark.sql.Column` expression to be removed from the array
.. versionchanged:: 4.0.0
`element` now also accepts a Column type.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that is an array excluding the given value from the input column.
Examples
--------
Example 1: Removing a specific value from a simple array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3, 1, 1],)], ['data'])
>>> df.select(sf.array_remove(df.data, 1)).show()
+---------------------+
|array_remove(data, 1)|
+---------------------+
| [2, 3]|
+---------------------+
Example 2: Removing a specific value from multiple arrays
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3, 1, 1],), ([4, 5, 5, 4],)], ['data'])
>>> df.select(sf.array_remove(df.data, 5)).show()
+---------------------+
|array_remove(data, 5)|
+---------------------+
| [1, 2, 3, 1, 1]|
| [4, 4]|
+---------------------+
Example 3: Removing a value that does not exist in the array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3],)], ['data'])
>>> df.select(sf.array_remove(df.data, 4)).show()
+---------------------+
|array_remove(data, 4)|
+---------------------+
| [1, 2, 3]|
+---------------------+
Example 4: Removing a value from an array with all identical values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 1, 1],)], ['data'])
>>> df.select(sf.array_remove(df.data, 1)).show()
+---------------------+
|array_remove(data, 1)|
+---------------------+
| []|
+---------------------+
Example 5: Removing a value from an empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", ArrayType(IntegerType()), True)
... ])
>>> df = spark.createDataFrame([([],)], schema)
>>> df.select(sf.array_remove(df.data, 1)).show()
+---------------------+
|array_remove(data, 1)|
+---------------------+
| []|
+---------------------+
Example 6: Removing a column's value from a simple array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3, 1, 1], 1)], ['data', 'col'])
>>> df.select(sf.array_remove(df.data, df.col)).show()
+-----------------------+
|array_remove(data, col)|
+-----------------------+
| [2, 3]|
+-----------------------+
"""
return _invoke_function_over_columns("array_remove", col, lit(element))
@_try_remote_functions
def array_distinct(col: "ColumnOrName") -> Column:
"""
Array function: removes duplicate values from the array.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
Returns
-------
:class:`~pyspark.sql.Column`
A new column that is an array of unique values from the input column.
Examples
--------
Example 1: Removing duplicate values from a simple array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3, 2],)], ['data'])
>>> df.select(sf.array_distinct(df.data)).show()
+--------------------+
|array_distinct(data)|
+--------------------+
| [1, 2, 3]|
+--------------------+
Example 2: Removing duplicate values from multiple arrays
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3, 2],), ([4, 5, 5, 4],)], ['data'])
>>> df.select(sf.array_distinct(df.data)).show()
+--------------------+
|array_distinct(data)|
+--------------------+
| [1, 2, 3]|
| [4, 5]|
+--------------------+
Example 3: Removing duplicate values from an array with all identical values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 1, 1],)], ['data'])
>>> df.select(sf.array_distinct(df.data)).show()
+--------------------+
|array_distinct(data)|
+--------------------+
| [1]|
+--------------------+
Example 4: Removing duplicate values from an array with no duplicate values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3],)], ['data'])
>>> df.select(sf.array_distinct(df.data)).show()
+--------------------+
|array_distinct(data)|
+--------------------+
| [1, 2, 3]|
+--------------------+
Example 5: Removing duplicate values from an empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", ArrayType(IntegerType()), True)
... ])
>>> df = spark.createDataFrame([([],)], schema)
>>> df.select(sf.array_distinct(df.data)).show()
+--------------------+
|array_distinct(data)|
+--------------------+
| []|
+--------------------+
"""
return _invoke_function_over_columns("array_distinct", col)
@_try_remote_functions
def array_insert(arr: "ColumnOrName", pos: Union["ColumnOrName", int], value: Any) -> Column:
"""
Array function: Inserts an item into a given array at a specified array index.
Array indices start at 1, or start from the end if index is negative.
Index above array size appends the array, or prepends the array if index is negative,
with 'null' elements.
.. versionadded:: 3.4.0
Parameters
----------
arr : :class:`~pyspark.sql.Column` or str
name of column containing an array
pos : :class:`~pyspark.sql.Column` or str or int
name of Numeric type column indicating position of insertion
(starting at index 1, negative position is a start from the back of the array)
value :
a literal value, or a :class:`~pyspark.sql.Column` expression.
Returns
-------
:class:`~pyspark.sql.Column`
an array of values, including the new specified value
Notes
-----
Supports Spark Connect.
Examples
--------
Example 1: Inserting a value at a specific position
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['a', 'b', 'c'],)], ['data'])
>>> df.select(sf.array_insert(df.data, 2, 'd')).show()
+------------------------+
|array_insert(data, 2, d)|
+------------------------+
| [a, d, b, c]|
+------------------------+
Example 2: Inserting a value at a negative position
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['a', 'b', 'c'],)], ['data'])
>>> df.select(sf.array_insert(df.data, -2, 'd')).show()
+-------------------------+
|array_insert(data, -2, d)|
+-------------------------+
| [a, b, d, c]|
+-------------------------+
Example 3: Inserting a value at a position greater than the array size
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['a', 'b', 'c'],)], ['data'])
>>> df.select(sf.array_insert(df.data, 5, 'e')).show()
+------------------------+
|array_insert(data, 5, e)|
+------------------------+
| [a, b, c, NULL, e]|
+------------------------+
Example 4: Inserting a NULL value
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['a', 'b', 'c'],)], ['data'])
>>> df.select(sf.array_insert(df.data, 2, sf.lit(None))).show()
+---------------------------+
|array_insert(data, 2, NULL)|
+---------------------------+
| [a, NULL, b, c]|
+---------------------------+
Example 5: Inserting a value into a NULL array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([StructField("data", ArrayType(IntegerType()), True)])
>>> df = spark.createDataFrame([(None,)], schema=schema)
>>> df.select(sf.array_insert(df.data, 1, 5)).show()
+------------------------+
|array_insert(data, 1, 5)|
+------------------------+
| NULL|
+------------------------+
"""
pos = _enum_to_value(pos)
pos = lit(pos) if isinstance(pos, int) else pos
return _invoke_function_over_columns("array_insert", arr, pos, lit(value))
@_try_remote_functions
def array_intersect(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""
Array function: returns a new array containing the intersection of elements in col1 and col2,
without duplicates.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or str
Name of column containing the first array.
col2 : :class:`~pyspark.sql.Column` or str
Name of column containing the second array.
Returns
-------
:class:`~pyspark.sql.Column`
A new array containing the intersection of elements in col1 and col2.
Notes
-----
This function does not preserve the order of the elements in the input arrays.
Examples
--------
Example 1: Basic usage
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["c", "d", "a", "f"])])
>>> df.select(sf.sort_array(sf.array_intersect(df.c1, df.c2))).show()
+-----------------------------------------+
|sort_array(array_intersect(c1, c2), true)|
+-----------------------------------------+
| [a, c]|
+-----------------------------------------+
Example 2: Intersection with no common elements
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["d", "e", "f"])])
>>> df.select(sf.array_intersect(df.c1, df.c2)).show()
+-----------------------+
|array_intersect(c1, c2)|
+-----------------------+
| []|
+-----------------------+
Example 3: Intersection with all common elements
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["a", "b", "c"], c2=["a", "b", "c"])])
>>> df.select(sf.sort_array(sf.array_intersect(df.c1, df.c2))).show()
+-----------------------------------------+
|sort_array(array_intersect(c1, c2), true)|
+-----------------------------------------+
| [a, b, c]|
+-----------------------------------------+
Example 4: Intersection with null values
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["a", "b", None], c2=["a", None, "c"])])
>>> df.select(sf.sort_array(sf.array_intersect(df.c1, df.c2))).show()
+-----------------------------------------+
|sort_array(array_intersect(c1, c2), true)|
+-----------------------------------------+
| [NULL, a]|
+-----------------------------------------+
Example 5: Intersection with empty arrays
>>> from pyspark.sql import Row, functions as sf
>>> from pyspark.sql.types import ArrayType, StringType, StructField, StructType
>>> data = [Row(c1=[], c2=["a", "b", "c"])]
>>> schema = StructType([
... StructField("c1", ArrayType(StringType()), True),
... StructField("c2", ArrayType(StringType()), True)
... ])
>>> df = spark.createDataFrame(data, schema)
>>> df.select(sf.array_intersect(df.c1, df.c2)).show()
+-----------------------+
|array_intersect(c1, c2)|
+-----------------------+
| []|
+-----------------------+
"""
return _invoke_function_over_columns("array_intersect", col1, col2)
@_try_remote_functions
def array_union(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""
Array function: returns a new array containing the union of elements in col1 and col2,
without duplicates.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or str
Name of column containing the first array.
col2 : :class:`~pyspark.sql.Column` or str
Name of column containing the second array.
Returns
-------
:class:`~pyspark.sql.Column`
A new array containing the union of elements in col1 and col2.
Notes
-----
This function does not preserve the order of the elements in the input arrays.
Examples
--------
Example 1: Basic usage
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["c", "d", "a", "f"])])
>>> df.select(sf.sort_array(sf.array_union(df.c1, df.c2))).show()
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c, d, f]|
+-------------------------------------+
Example 2: Union with no common elements
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["d", "e", "f"])])
>>> df.select(sf.sort_array(sf.array_union(df.c1, df.c2))).show()
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c, d, e, f]|
+-------------------------------------+
Example 3: Union with all common elements
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["a", "b", "c"], c2=["a", "b", "c"])])
>>> df.select(sf.sort_array(sf.array_union(df.c1, df.c2))).show()
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c]|
+-------------------------------------+
Example 4: Union with null values
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["a", "b", None], c2=["a", None, "c"])])
>>> df.select(sf.sort_array(sf.array_union(df.c1, df.c2))).show()
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [NULL, a, b, c]|
+-------------------------------------+
Example 5: Union with empty arrays
>>> from pyspark.sql import Row, functions as sf
>>> from pyspark.sql.types import ArrayType, StringType, StructField, StructType
>>> data = [Row(c1=[], c2=["a", "b", "c"])]
>>> schema = StructType([
... StructField("c1", ArrayType(StringType()), True),
... StructField("c2", ArrayType(StringType()), True)
... ])
>>> df = spark.createDataFrame(data, schema)
>>> df.select(sf.sort_array(sf.array_union(df.c1, df.c2))).show()
+-------------------------------------+
|sort_array(array_union(c1, c2), true)|
+-------------------------------------+
| [a, b, c]|
+-------------------------------------+
"""
return _invoke_function_over_columns("array_union", col1, col2)
@_try_remote_functions
def array_except(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""
Array function: returns a new array containing the elements present in col1 but not in col2,
without duplicates.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or str
Name of column containing the first array.
col2 : :class:`~pyspark.sql.Column` or str
Name of column containing the second array.
Returns
-------
:class:`~pyspark.sql.Column`
A new array containing the elements present in col1 but not in col2.
Notes
-----
This function does not preserve the order of the elements in the input arrays.
Examples
--------
Example 1: Basic usage
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["c", "d", "a", "f"])])
>>> df.select(sf.array_except(df.c1, df.c2)).show()
+--------------------+
|array_except(c1, c2)|
+--------------------+
| [b]|
+--------------------+
Example 2: Except with no common elements
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["d", "e", "f"])])
>>> df.select(sf.sort_array(sf.array_except(df.c1, df.c2))).show()
+--------------------------------------+
|sort_array(array_except(c1, c2), true)|
+--------------------------------------+
| [a, b, c]|
+--------------------------------------+
Example 3: Except with all common elements
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["a", "b", "c"], c2=["a", "b", "c"])])
>>> df.select(sf.array_except(df.c1, df.c2)).show()
+--------------------+
|array_except(c1, c2)|
+--------------------+
| []|
+--------------------+
Example 4: Except with null values
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["a", "b", None], c2=["a", None, "c"])])
>>> df.select(sf.array_except(df.c1, df.c2)).show()
+--------------------+
|array_except(c1, c2)|
+--------------------+
| [b]|
+--------------------+
Example 5: Except with empty arrays
>>> from pyspark.sql import Row, functions as sf
>>> from pyspark.sql.types import ArrayType, StringType, StructField, StructType
>>> data = [Row(c1=[], c2=["a", "b", "c"])]
>>> schema = StructType([
... StructField("c1", ArrayType(StringType()), True),
... StructField("c2", ArrayType(StringType()), True)
... ])
>>> df = spark.createDataFrame(data, schema)
>>> df.select(sf.array_except(df.c1, df.c2)).show()
+--------------------+
|array_except(c1, c2)|
+--------------------+
| []|
+--------------------+
"""
return _invoke_function_over_columns("array_except", col1, col2)
@_try_remote_functions
def array_compact(col: "ColumnOrName") -> Column:
"""
Array function: removes null values from the array.
.. versionadded:: 3.4.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
Returns
-------
:class:`~pyspark.sql.Column`
A new column that is an array excluding the null values from the input column.
Notes
-----
Supports Spark Connect.
Examples
--------
Example 1: Removing null values from a simple array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, None, 2, 3],)], ['data'])
>>> df.select(sf.array_compact(df.data)).show()
+-------------------+
|array_compact(data)|
+-------------------+
| [1, 2, 3]|
+-------------------+
Example 2: Removing null values from multiple arrays
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, None, 2, 3],), ([4, 5, None, 4],)], ['data'])
>>> df.select(sf.array_compact(df.data)).show()
+-------------------+
|array_compact(data)|
+-------------------+
| [1, 2, 3]|
| [4, 5, 4]|
+-------------------+
Example 3: Removing null values from an array with all null values
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, StringType, StructField, StructType
>>> schema = StructType([
... StructField("data", ArrayType(StringType()), True)
... ])
>>> df = spark.createDataFrame([([None, None, None],)], schema)
>>> df.select(sf.array_compact(df.data)).show()
+-------------------+
|array_compact(data)|
+-------------------+
| []|
+-------------------+
Example 4: Removing null values from an array with no null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3],)], ['data'])
>>> df.select(sf.array_compact(df.data)).show()
+-------------------+
|array_compact(data)|
+-------------------+
| [1, 2, 3]|
+-------------------+
Example 5: Removing null values from an empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, StringType, StructField, StructType
>>> schema = StructType([
... StructField("data", ArrayType(StringType()), True)
... ])
>>> df = spark.createDataFrame([([],)], schema)
>>> df.select(sf.array_compact(df.data)).show()
+-------------------+
|array_compact(data)|
+-------------------+
| []|
+-------------------+
"""
return _invoke_function_over_columns("array_compact", col)
@_try_remote_functions
def array_append(col: "ColumnOrName", value: Any) -> Column:
"""
Array function: returns a new array column by appending `value` to the existing array `col`.
.. versionadded:: 3.4.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The name of the column containing the array.
value :
A literal value, or a :class:`~pyspark.sql.Column` expression to be appended to the array.
Returns
-------
:class:`~pyspark.sql.Column`
A new array column with `value` appended to the original array.
Notes
-----
Supports Spark Connect.
Examples
--------
Example 1: Appending a column value to an array column
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2="c")])
>>> df.select(sf.array_append(df.c1, df.c2)).show()
+--------------------+
|array_append(c1, c2)|
+--------------------+
| [b, a, c, c]|
+--------------------+
Example 2: Appending a numeric value to an array column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3],)], ['data'])
>>> df.select(sf.array_append(df.data, 4)).show()
+---------------------+
|array_append(data, 4)|
+---------------------+
| [1, 2, 3, 4]|
+---------------------+
Example 3: Appending a null value to an array column
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3],)], ['data'])
>>> df.select(sf.array_append(df.data, None)).show()
+------------------------+
|array_append(data, NULL)|
+------------------------+
| [1, 2, 3, NULL]|
+------------------------+
Example 4: Appending a value to a NULL array column
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", ArrayType(IntegerType()), True)
... ])
>>> df = spark.createDataFrame([(None,)], schema=schema)
>>> df.select(sf.array_append(df.data, 4)).show()
+---------------------+
|array_append(data, 4)|
+---------------------+
| NULL|
+---------------------+
Example 5: Appending a value to an empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", ArrayType(IntegerType()), True)
... ])
>>> df = spark.createDataFrame([([],)], schema=schema)
>>> df.select(sf.array_append(df.data, 1)).show()
+---------------------+
|array_append(data, 1)|
+---------------------+
| [1]|
+---------------------+
"""
return _invoke_function_over_columns("array_append", col, lit(value))
@_try_remote_functions
def explode(col: "ColumnOrName") -> Column:
"""
Returns a new row for each element in the given array or map.
Uses the default column name `col` for elements in the array and
`key` and `value` for elements in the map unless specified otherwise.
.. versionadded:: 1.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
One row per array item or map key value.
See Also
--------
:meth:`pyspark.sql.functions.posexplode`
:meth:`pyspark.sql.functions.explode_outer`
:meth:`pyspark.sql.functions.posexplode_outer`
:meth:`pyspark.sql.functions.inline`
:meth:`pyspark.sql.functions.inline_outer`
Notes
-----
Only one explode is allowed per SELECT clause.
Examples
--------
Example 1: Exploding an array column
>>> from pyspark.sql import functions as sf
>>> df = spark.sql('SELECT * FROM VALUES (1,ARRAY(1,2,3,NULL)), (2,ARRAY()), (3,NULL) AS t(i,a)')
>>> df.show()
+---+---------------+
| i| a|
+---+---------------+
| 1|[1, 2, 3, NULL]|
| 2| []|
| 3| NULL|
+---+---------------+
>>> df.select('*', sf.explode('a')).show()
+---+---------------+----+
| i| a| col|
+---+---------------+----+
| 1|[1, 2, 3, NULL]| 1|
| 1|[1, 2, 3, NULL]| 2|
| 1|[1, 2, 3, NULL]| 3|
| 1|[1, 2, 3, NULL]|NULL|
+---+---------------+----+
Example 2: Exploding a map column
>>> from pyspark.sql import functions as sf
>>> df = spark.sql('SELECT * FROM VALUES (1,MAP(1,2,3,4,5,NULL)), (2,MAP()), (3,NULL) AS t(i,m)')
>>> df.show(truncate=False)
+---+---------------------------+
|i |m |
+---+---------------------------+
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|
|2 |{} |
|3 |NULL |
+---+---------------------------+
>>> df.select('*', sf.explode('m')).show(truncate=False)
+---+---------------------------+---+-----+
|i |m |key|value|
+---+---------------------------+---+-----+
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|1 |2 |
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|3 |4 |
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|5 |NULL |
+---+---------------------------+---+-----+
Example 3: Exploding multiple array columns
>>> import pyspark.sql.functions as sf
>>> df = spark.sql('SELECT ARRAY(1,2) AS a1, ARRAY(3,4,5) AS a2')
>>> df.select(
... '*', sf.explode('a1').alias('v1')
... ).select('*', sf.explode('a2').alias('v2')).show()
+------+---------+---+---+
| a1| a2| v1| v2|
+------+---------+---+---+
|[1, 2]|[3, 4, 5]| 1| 3|
|[1, 2]|[3, 4, 5]| 1| 4|
|[1, 2]|[3, 4, 5]| 1| 5|
|[1, 2]|[3, 4, 5]| 2| 3|
|[1, 2]|[3, 4, 5]| 2| 4|
|[1, 2]|[3, 4, 5]| 2| 5|
+------+---------+---+---+
Example 4: Exploding an array of struct column
>>> import pyspark.sql.functions as sf
>>> df = spark.sql('SELECT ARRAY(NAMED_STRUCT("a",1,"b",2), NAMED_STRUCT("a",3,"b",4)) AS a')
>>> df.select(sf.explode('a').alias("s")).select("s.*").show()
+---+---+
| a| b|
+---+---+
| 1| 2|
| 3| 4|
+---+---+
""" # noqa: E501
return _invoke_function_over_columns("explode", col)
@_try_remote_functions
def posexplode(col: "ColumnOrName") -> Column:
"""
Returns a new row for each element with position in the given array or map.
Uses the default column name `pos` for position, and `col` for elements in the
array and `key` and `value` for elements in the map unless specified otherwise.
.. versionadded:: 2.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
one row per array item or map key value including positions as a separate column.
See Also
--------
:meth:`pyspark.sql.functions.explode`
:meth:`pyspark.sql.functions.explode_outer`
:meth:`pyspark.sql.functions.posexplode_outer`
:meth:`pyspark.sql.functions.inline`
:meth:`pyspark.sql.functions.inline_outer`
Examples
--------
Example 1: Exploding an array column
>>> from pyspark.sql import functions as sf
>>> df = spark.sql('SELECT * FROM VALUES (1,ARRAY(1,2,3,NULL)), (2,ARRAY()), (3,NULL) AS t(i,a)')
>>> df.show()
+---+---------------+
| i| a|
+---+---------------+
| 1|[1, 2, 3, NULL]|
| 2| []|
| 3| NULL|
+---+---------------+
>>> df.select('*', sf.posexplode('a')).show()
+---+---------------+---+----+
| i| a|pos| col|
+---+---------------+---+----+
| 1|[1, 2, 3, NULL]| 0| 1|
| 1|[1, 2, 3, NULL]| 1| 2|
| 1|[1, 2, 3, NULL]| 2| 3|
| 1|[1, 2, 3, NULL]| 3|NULL|
+---+---------------+---+----+
Example 2: Exploding a map column
>>> from pyspark.sql import functions as sf
>>> df = spark.sql('SELECT * FROM VALUES (1,MAP(1,2,3,4,5,NULL)), (2,MAP()), (3,NULL) AS t(i,m)')
>>> df.show(truncate=False)
+---+---------------------------+
|i |m |
+---+---------------------------+
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|
|2 |{} |
|3 |NULL |
+---+---------------------------+
>>> df.select('*', sf.posexplode('m')).show(truncate=False)
+---+---------------------------+---+---+-----+
|i |m |pos|key|value|
+---+---------------------------+---+---+-----+
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|0 |1 |2 |
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|1 |3 |4 |
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|2 |5 |NULL |
+---+---------------------------+---+---+-----+
""" # noqa: E501
return _invoke_function_over_columns("posexplode", col)
@_try_remote_functions
def inline(col: "ColumnOrName") -> Column:
"""
Explodes an array of structs into a table.
This function takes an input column containing an array of structs and returns a
new column where each struct in the array is exploded into a separate row.
.. versionadded:: 3.4.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Input column of values to explode.
Returns
-------
:class:`~pyspark.sql.Column`
Generator expression with the inline exploded result.
See Also
--------
:meth:`pyspark.sql.functions.explode`
:meth:`pyspark.sql.functions.explode_outer`
:meth:`pyspark.sql.functions.posexplode`
:meth:`pyspark.sql.functions.posexplode_outer`
:meth:`pyspark.sql.functions.inline_outer`
Examples
--------
Example 1: Using inline with a single struct array column
>>> import pyspark.sql.functions as sf
>>> df = spark.sql('SELECT ARRAY(NAMED_STRUCT("a",1,"b",2), NAMED_STRUCT("a",3,"b",4)) AS a')
>>> df.select('*', sf.inline(df.a)).show()
+----------------+---+---+
| a| a| b|
+----------------+---+---+
|[{1, 2}, {3, 4}]| 1| 2|
|[{1, 2}, {3, 4}]| 3| 4|
+----------------+---+---+
Example 2: Using inline with a column name
>>> import pyspark.sql.functions as sf
>>> df = spark.sql('SELECT ARRAY(NAMED_STRUCT("a",1,"b",2), NAMED_STRUCT("a",3,"b",4)) AS a')
>>> df.select('*', sf.inline('a')).show()
+----------------+---+---+
| a| a| b|
+----------------+---+---+
|[{1, 2}, {3, 4}]| 1| 2|
|[{1, 2}, {3, 4}]| 3| 4|
+----------------+---+---+
Example 3: Using inline with an alias
>>> import pyspark.sql.functions as sf
>>> df = spark.sql('SELECT ARRAY(NAMED_STRUCT("a",1,"b",2), NAMED_STRUCT("a",3,"b",4)) AS a')
>>> df.select('*', sf.inline('a').alias("c1", "c2")).show()
+----------------+---+---+
| a| c1| c2|
+----------------+---+---+
|[{1, 2}, {3, 4}]| 1| 2|
|[{1, 2}, {3, 4}]| 3| 4|
+----------------+---+---+
Example 4: Using inline with multiple struct array columns
>>> import pyspark.sql.functions as sf
>>> df = spark.sql('SELECT ARRAY(NAMED_STRUCT("a",1,"b",2), NAMED_STRUCT("a",3,"b",4)) AS a1, ARRAY(NAMED_STRUCT("c",5,"d",6), NAMED_STRUCT("c",7,"d",8)) AS a2')
>>> df.select(
... '*', sf.inline('a1')
... ).select('*', sf.inline('a2')).show()
+----------------+----------------+---+---+---+---+
| a1| a2| a| b| c| d|
+----------------+----------------+---+---+---+---+
|[{1, 2}, {3, 4}]|[{5, 6}, {7, 8}]| 1| 2| 5| 6|
|[{1, 2}, {3, 4}]|[{5, 6}, {7, 8}]| 1| 2| 7| 8|
|[{1, 2}, {3, 4}]|[{5, 6}, {7, 8}]| 3| 4| 5| 6|
|[{1, 2}, {3, 4}]|[{5, 6}, {7, 8}]| 3| 4| 7| 8|
+----------------+----------------+---+---+---+---+
Example 5: Using inline with a nested struct array column
>>> import pyspark.sql.functions as sf
>>> df = spark.sql('SELECT NAMED_STRUCT("a",1,"b",2,"c",ARRAY(NAMED_STRUCT("c",3,"d",4), NAMED_STRUCT("c",5,"d",6))) AS s')
>>> df.select('*', sf.inline('s.c')).show(truncate=False)
+------------------------+---+---+
|s |c |d |
+------------------------+---+---+
|{1, 2, [{3, 4}, {5, 6}]}|3 |4 |
|{1, 2, [{3, 4}, {5, 6}]}|5 |6 |
+------------------------+---+---+
Example 6: Using inline with a column containing: array continaing null, empty array and null
>>> from pyspark.sql import functions as sf
>>> df = spark.sql('SELECT * FROM VALUES (1,ARRAY(NAMED_STRUCT("a",1,"b",2), NULL, NAMED_STRUCT("a",3,"b",4))), (2,ARRAY()), (3,NULL) AS t(i,s)')
>>> df.show(truncate=False)
+---+----------------------+
|i |s |
+---+----------------------+
|1 |[{1, 2}, NULL, {3, 4}]|
|2 |[] |
|3 |NULL |
+---+----------------------+
>>> df.select('*', sf.inline('s')).show(truncate=False)
+---+----------------------+----+----+
|i |s |a |b |
+---+----------------------+----+----+
|1 |[{1, 2}, NULL, {3, 4}]|1 |2 |
|1 |[{1, 2}, NULL, {3, 4}]|NULL|NULL|
|1 |[{1, 2}, NULL, {3, 4}]|3 |4 |
+---+----------------------+----+----+
""" # noqa: E501
return _invoke_function_over_columns("inline", col)
@_try_remote_functions
def explode_outer(col: "ColumnOrName") -> Column:
"""
Returns a new row for each element in the given array or map.
Unlike explode, if the array/map is null or empty then null is produced.
Uses the default column name `col` for elements in the array and
`key` and `value` for elements in the map unless specified otherwise.
.. versionadded:: 2.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
one row per array item or map key value.
See Also
--------
:meth:`pyspark.sql.functions.explode`
:meth:`pyspark.sql.functions.posexplode`
:meth:`pyspark.sql.functions.posexplode_outer`
:meth:`pyspark.sql.functions.inline`
:meth:`pyspark.sql.functions.inline_outer`
Examples
--------
Example 1: Using an array column
>>> from pyspark.sql import functions as sf
>>> df = spark.sql('SELECT * FROM VALUES (1,ARRAY(1,2,3,NULL)), (2,ARRAY()), (3,NULL) AS t(i,a)')
>>> df.select('*', sf.explode_outer('a')).show()
+---+---------------+----+
| i| a| col|
+---+---------------+----+
| 1|[1, 2, 3, NULL]| 1|
| 1|[1, 2, 3, NULL]| 2|
| 1|[1, 2, 3, NULL]| 3|
| 1|[1, 2, 3, NULL]|NULL|
| 2| []|NULL|
| 3| NULL|NULL|
+---+---------------+----+
Example 2: Using a map column
>>> from pyspark.sql import functions as sf
>>> df = spark.sql('SELECT * FROM VALUES (1,MAP(1,2,3,4,5,NULL)), (2,MAP()), (3,NULL) AS t(i,m)')
>>> df.select('*', sf.explode_outer('m')).show(truncate=False)
+---+---------------------------+----+-----+
|i |m |key |value|
+---+---------------------------+----+-----+
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|1 |2 |
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|3 |4 |
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|5 |NULL |
|2 |{} |NULL|NULL |
|3 |NULL |NULL|NULL |
+---+---------------------------+----+-----+
""" # noqa: E501
return _invoke_function_over_columns("explode_outer", col)
@_try_remote_functions
def posexplode_outer(col: "ColumnOrName") -> Column:
"""
Returns a new row for each element with position in the given array or map.
Unlike posexplode, if the array/map is null or empty then the row (null, null) is produced.
Uses the default column name `pos` for position, and `col` for elements in the
array and `key` and `value` for elements in the map unless specified otherwise.
.. versionadded:: 2.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
target column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
one row per array item or map key value including positions as a separate column.
See Also
--------
:meth:`pyspark.sql.functions.explode`
:meth:`pyspark.sql.functions.explode_outer`
:meth:`pyspark.sql.functions.posexplode`
:meth:`pyspark.sql.functions.inline`
:meth:`pyspark.sql.functions.inline_outer`
Examples
--------
Example 1: Using an array column
>>> from pyspark.sql import functions as sf
>>> df = spark.sql('SELECT * FROM VALUES (1,ARRAY(1,2,3,NULL)), (2,ARRAY()), (3,NULL) AS t(i,a)')
>>> df.select('*', sf.posexplode_outer('a')).show()
+---+---------------+----+----+
| i| a| pos| col|
+---+---------------+----+----+
| 1|[1, 2, 3, NULL]| 0| 1|
| 1|[1, 2, 3, NULL]| 1| 2|
| 1|[1, 2, 3, NULL]| 2| 3|
| 1|[1, 2, 3, NULL]| 3|NULL|
| 2| []|NULL|NULL|
| 3| NULL|NULL|NULL|
+---+---------------+----+----+
Example 2: Using a map column
>>> from pyspark.sql import functions as sf
>>> df = spark.sql('SELECT * FROM VALUES (1,MAP(1,2,3,4,5,NULL)), (2,MAP()), (3,NULL) AS t(i,m)')
>>> df.select('*', sf.posexplode_outer('m')).show(truncate=False)
+---+---------------------------+----+----+-----+
|i |m |pos |key |value|
+---+---------------------------+----+----+-----+
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|0 |1 |2 |
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|1 |3 |4 |
|1 |{1 -> 2, 3 -> 4, 5 -> NULL}|2 |5 |NULL |
|2 |{} |NULL|NULL|NULL |
|3 |NULL |NULL|NULL|NULL |
+---+---------------------------+----+----+-----+
""" # noqa: E501
return _invoke_function_over_columns("posexplode_outer", col)
@_try_remote_functions
def inline_outer(col: "ColumnOrName") -> Column:
"""
Explodes an array of structs into a table.
Unlike inline, if the array is null or empty then null is produced for each nested column.
.. versionadded:: 3.4.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
input column of values to explode.
Returns
-------
:class:`~pyspark.sql.Column`
generator expression with the inline exploded result.
See Also
--------
:meth:`pyspark.sql.functions.explode`
:meth:`pyspark.sql.functions.explode_outer`
:meth:`pyspark.sql.functions.posexplode`
:meth:`pyspark.sql.functions.posexplode_outer`
:meth:`pyspark.sql.functions.inline`
Notes
-----
Supports Spark Connect.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.sql('SELECT * FROM VALUES (1,ARRAY(NAMED_STRUCT("a",1,"b",2), NULL, NAMED_STRUCT("a",3,"b",4))), (2,ARRAY()), (3,NULL) AS t(i,s)')
>>> df.printSchema()
root
|-- i: integer (nullable = false)
|-- s: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- a: integer (nullable = false)
| | |-- b: integer (nullable = false)
>>> df.select('*', sf.inline_outer('s')).show(truncate=False)
+---+----------------------+----+----+
|i |s |a |b |
+---+----------------------+----+----+
|1 |[{1, 2}, NULL, {3, 4}]|1 |2 |
|1 |[{1, 2}, NULL, {3, 4}]|NULL|NULL|
|1 |[{1, 2}, NULL, {3, 4}]|3 |4 |
|2 |[] |NULL|NULL|
|3 |NULL |NULL|NULL|
+---+----------------------+----+----+
""" # noqa: E501
return _invoke_function_over_columns("inline_outer", col)
@_try_remote_functions
def get_json_object(col: "ColumnOrName", path: str) -> Column:
"""
Extracts json object from a json string based on json `path` specified, and returns json string
of the extracted json object. It will return null if the input json string is invalid.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
string column in json format
path : str
path to the json object to extract
Returns
-------
:class:`~pyspark.sql.Column`
string representation of given JSON object value.
Examples
--------
Example 1: Extract a json object from json string
>>> data = [("1", '''{"f1": "value1", "f2": "value2"}'''), ("2", '''{"f1": "value12"}''')]
>>> df = spark.createDataFrame(data, ("key", "jstring"))
>>> df.select(df.key,
... get_json_object(df.jstring, '$.f1').alias("c0"),
... get_json_object(df.jstring, '$.f2').alias("c1")
... ).show()
+---+-------+------+
|key| c0| c1|
+---+-------+------+
| 1| value1|value2|
| 2|value12| NULL|
+---+-------+------+
Example 2: Extract a json object from json array
>>> data = [
... ("1", '''[{"f1": "value1"},{"f1": "value2"}]'''),
... ("2", '''[{"f1": "value12"},{"f2": "value13"}]''')
... ]
>>> df = spark.createDataFrame(data, ("key", "jarray"))
>>> df.select(df.key,
... get_json_object(df.jarray, '$[0].f1').alias("c0"),
... get_json_object(df.jarray, '$[1].f2').alias("c1")
... ).show()
+---+-------+-------+
|key| c0| c1|
+---+-------+-------+
| 1| value1| NULL|
| 2|value12|value13|
+---+-------+-------+
>>> df.select(df.key,
... get_json_object(df.jarray, '$[*].f1').alias("c0"),
... get_json_object(df.jarray, '$[*].f2').alias("c1")
... ).show()
+---+-------------------+---------+
|key| c0| c1|
+---+-------------------+---------+
| 1|["value1","value2"]| NULL|
| 2| "value12"|"value13"|
+---+-------------------+---------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("get_json_object", _to_java_column(col), _enum_to_value(path))
@_try_remote_functions
def json_tuple(col: "ColumnOrName", *fields: str) -> Column:
"""Creates a new row for a json column according to the given field names.
.. versionadded:: 1.6.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
string column in json format
fields : str
a field or fields to extract
Returns
-------
:class:`~pyspark.sql.Column`
a new row for each given field value from json object
Examples
--------
>>> data = [("1", '''{"f1": "value1", "f2": "value2"}'''), ("2", '''{"f1": "value12"}''')]
>>> df = spark.createDataFrame(data, ("key", "jstring"))
>>> df.select(df.key, json_tuple(df.jstring, 'f1', 'f2')).collect()
[Row(key='1', c0='value1', c1='value2'), Row(key='2', c0='value12', c1=None)]
"""
from pyspark.sql.classic.column import _to_seq, _to_java_column
if len(fields) == 0:
raise PySparkValueError(
errorClass="CANNOT_BE_EMPTY",
messageParameters={"item": "field"},
)
sc = _get_active_spark_context()
return _invoke_function("json_tuple", _to_java_column(col), _to_seq(sc, fields))
@_try_remote_functions
def from_json(
col: "ColumnOrName",
schema: Union[ArrayType, StructType, Column, str],
options: Optional[Mapping[str, str]] = None,
) -> Column:
"""
Parses a column containing a JSON string into a :class:`MapType` with :class:`StringType`
as keys type, :class:`StructType` or :class:`ArrayType` with
the specified schema. Returns `null`, in the case of an unparsable string.
.. versionadded:: 2.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
a column or column name in JSON format
schema : :class:`DataType` or str
a StructType, ArrayType of StructType or Python string literal with a DDL-formatted string
to use when parsing the json column
options : dict, optional
options to control parsing. accepts the same options as the json datasource.
See `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option>`_
for the version you use.
.. # noqa
Returns
-------
:class:`~pyspark.sql.Column`
a new column of complex type from given JSON object.
Examples
--------
Example 1: Parsing JSON with a specified schema
>>> import pyspark.sql.functions as sf
>>> from pyspark.sql.types import StructType, StructField, IntegerType
>>> schema = StructType([StructField("a", IntegerType())])
>>> df = spark.createDataFrame([(1, '''{"a": 1}''')], ("key", "value"))
>>> df.select(sf.from_json(df.value, schema).alias("json")).show()
+----+
|json|
+----+
| {1}|
+----+
Example 2: Parsing JSON with a DDL-formatted string.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1, '''{"a": 1}''')], ("key", "value"))
>>> df.select(sf.from_json(df.value, "a INT").alias("json")).show()
+----+
|json|
+----+
| {1}|
+----+
Example 3: Parsing JSON into a MapType
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1, '''{"a": 1}''')], ("key", "value"))
>>> df.select(sf.from_json(df.value, "MAP<STRING,INT>").alias("json")).show()
+--------+
| json|
+--------+
|{a -> 1}|
+--------+
Example 4: Parsing JSON into an ArrayType of StructType
>>> import pyspark.sql.functions as sf
>>> from pyspark.sql.types import ArrayType, StructType, StructField, IntegerType
>>> schema = ArrayType(StructType([StructField("a", IntegerType())]))
>>> df = spark.createDataFrame([(1, '''[{"a": 1}]''')], ("key", "value"))
>>> df.select(sf.from_json(df.value, schema).alias("json")).show()
+-----+
| json|
+-----+
|[{1}]|
+-----+
Example 5: Parsing JSON into an ArrayType
>>> import pyspark.sql.functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType
>>> schema = ArrayType(IntegerType())
>>> df = spark.createDataFrame([(1, '''[1, 2, 3]''')], ("key", "value"))
>>> df.select(sf.from_json(df.value, schema).alias("json")).show()
+---------+
| json|
+---------+
|[1, 2, 3]|
+---------+
Example 6: Parsing JSON with specified options
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1, '''{a:123}'''), (2, '''{"a":456}''')], ("key", "value"))
>>> parsed1 = sf.from_json(df.value, "a INT")
>>> parsed2 = sf.from_json(df.value, "a INT", {"allowUnquotedFieldNames": "true"})
>>> df.select("value", parsed1, parsed2).show()
+---------+----------------+----------------+
| value|from_json(value)|from_json(value)|
+---------+----------------+----------------+
| {a:123}| {NULL}| {123}|
|{"a":456}| {456}| {456}|
+---------+----------------+----------------+
"""
from pyspark.sql.classic.column import _to_java_column
if isinstance(schema, DataType):
schema = schema.json()
elif isinstance(schema, Column):
schema = _to_java_column(schema)
return _invoke_function("from_json", _to_java_column(col), schema, _options_to_str(options))
@_try_remote_functions
def try_parse_json(
col: "ColumnOrName",
) -> Column:
"""
Parses a column containing a JSON string into a :class:`VariantType`. Returns None if a string
contains an invalid JSON value.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
a column or column name JSON formatted strings
Returns
-------
:class:`~pyspark.sql.Column`
a new column of VariantType.
Examples
--------
>>> df = spark.createDataFrame([ {'json': '''{ "a" : 1 }'''}, {'json': '''{a : 1}'''} ])
>>> df.select(to_json(try_parse_json(df.json))).collect()
[Row(to_json(try_parse_json(json))='{"a":1}'), Row(to_json(try_parse_json(json))=None)]
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("try_parse_json", _to_java_column(col))
@_try_remote_functions
def to_variant_object(
col: "ColumnOrName",
) -> Column:
"""
Converts a column containing nested inputs (array/map/struct) into a variants where maps and
structs are converted to variant objects which are unordered unlike SQL structs. Input maps can
only have string keys.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
a column with a nested schema or column name
Returns
-------
:class:`~pyspark.sql.Column`
a new column of VariantType.
Examples
--------
Example 1: Converting an array containing a nested struct into a variant
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, StructType, StructField, StringType, MapType
>>> schema = StructType([
... StructField("i", StringType(), True),
... StructField("v", ArrayType(StructType([
... StructField("a", MapType(StringType(), StringType()), True)
... ]), True))
... ])
>>> data = [("1", [{"a": {"b": 2}}])]
>>> df = spark.createDataFrame(data, schema)
>>> df.select(sf.to_variant_object(df.v))
DataFrame[to_variant_object(v): variant]
>>> df.select(sf.to_variant_object(df.v)).show(truncate=False)
+--------------------+
|to_variant_object(v)|
+--------------------+
|[{"a":{"b":"2"}}] |
+--------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("to_variant_object", _to_java_column(col))
@_try_remote_functions
def parse_json(
col: "ColumnOrName",
) -> Column:
"""
Parses a column containing a JSON string into a :class:`VariantType`. Throws exception if a
string represents an invalid JSON value.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
a column or column name JSON formatted strings
Returns
-------
:class:`~pyspark.sql.Column`
a new column of VariantType.
Examples
--------
>>> df = spark.createDataFrame([ {'json': '''{ "a" : 1 }'''} ])
>>> df.select(to_json(parse_json(df.json))).collect()
[Row(to_json(parse_json(json))='{"a":1}')]
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("parse_json", _to_java_column(col))
@_try_remote_functions
def is_variant_null(v: "ColumnOrName") -> Column:
"""
Check if a variant value is a variant null. Returns true if and only if the input is a variant
null and false otherwise (including in the case of SQL NULL).
.. versionadded:: 4.0.0
Parameters
----------
v : :class:`~pyspark.sql.Column` or str
a variant column or column name
Returns
-------
:class:`~pyspark.sql.Column`
a boolean column indicating whether the variant value is a variant null
Examples
--------
>>> df = spark.createDataFrame([ {'json': '''{ "a" : 1 }'''} ])
>>> df.select(is_variant_null(parse_json(df.json)).alias("r")).collect()
[Row(r=False)]
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("is_variant_null", _to_java_column(v))
@_try_remote_functions
def variant_get(v: "ColumnOrName", path: Union[Column, str], targetType: str) -> Column:
"""
Extracts a sub-variant from `v` according to `path`, and then cast the sub-variant to
`targetType`. Returns null if the path does not exist. Throws an exception if the cast fails.
.. versionadded:: 4.0.0
Parameters
----------
v : :class:`~pyspark.sql.Column` or str
a variant column or column name
path : :class:`~pyspark.sql.Column` or str
a column containing the extraction path strings or a string representing the extraction
path. A valid path should start with `$` and is followed by zero or more segments like
`[123]`, `.name`, `['name']`, or `["name"]`.
targetType : str
the target data type to cast into, in a DDL-formatted string
Returns
-------
:class:`~pyspark.sql.Column`
a column of `targetType` representing the extracted result
Examples
--------
>>> df = spark.createDataFrame([ {'json': '''{ "a" : 1 }''', 'path': '$.a'} ])
>>> df.select(variant_get(parse_json(df.json), "$.a", "int").alias("r")).collect()
[Row(r=1)]
>>> df.select(variant_get(parse_json(df.json), "$.b", "int").alias("r")).collect()
[Row(r=None)]
>>> df.select(variant_get(parse_json(df.json), df.path, "int").alias("r")).collect()
[Row(r=1)]
"""
from pyspark.sql.classic.column import _to_java_column
assert isinstance(path, (Column, str))
if isinstance(path, str):
return _invoke_function(
"variant_get", _to_java_column(v), _enum_to_value(path), _enum_to_value(targetType)
)
else:
return _invoke_function(
"variant_get", _to_java_column(v), _to_java_column(path), _enum_to_value(targetType)
)
@_try_remote_functions
def try_variant_get(v: "ColumnOrName", path: Union[Column, str], targetType: str) -> Column:
"""
Extracts a sub-variant from `v` according to `path`, and then cast the sub-variant to
`targetType`. Returns null if the path does not exist or the cast fails.
.. versionadded:: 4.0.0
Parameters
----------
v : :class:`~pyspark.sql.Column` or str
a variant column or column name
path : :class:`~pyspark.sql.Column` or str
a column containing the extraction path strings or a string representing the extraction
path. A valid path should start with `$` and is followed by zero or more segments like
`[123]`, `.name`, `['name']`, or `["name"]`.
targetType : str
the target data type to cast into, in a DDL-formatted string
Returns
-------
:class:`~pyspark.sql.Column`
a column of `targetType` representing the extracted result
Examples
--------
>>> df = spark.createDataFrame([ {'json': '''{ "a" : 1 }''', 'path': '$.a'} ])
>>> df.select(try_variant_get(parse_json(df.json), "$.a", "int").alias("r")).collect()
[Row(r=1)]
>>> df.select(try_variant_get(parse_json(df.json), "$.b", "int").alias("r")).collect()
[Row(r=None)]
>>> df.select(try_variant_get(parse_json(df.json), "$.a", "binary").alias("r")).collect()
[Row(r=None)]
>>> df.select(try_variant_get(parse_json(df.json), df.path, "int").alias("r")).collect()
[Row(r=1)]
"""
from pyspark.sql.classic.column import _to_java_column
if isinstance(path, str):
return _invoke_function(
"try_variant_get", _to_java_column(v), _enum_to_value(path), _enum_to_value(targetType)
)
else:
return _invoke_function(
"try_variant_get", _to_java_column(v), _to_java_column(path), _enum_to_value(targetType)
)
@_try_remote_functions
def schema_of_variant(v: "ColumnOrName") -> Column:
"""
Returns schema in the SQL format of a variant.
.. versionadded:: 4.0.0
Parameters
----------
v : :class:`~pyspark.sql.Column` or str
a variant column or column name
Returns
-------
:class:`~pyspark.sql.Column`
a string column representing the variant schema
Examples
--------
>>> df = spark.createDataFrame([ {'json': '''{ "a" : 1 }'''} ])
>>> df.select(schema_of_variant(parse_json(df.json)).alias("r")).collect()
[Row(r='OBJECT<a: BIGINT>')]
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("schema_of_variant", _to_java_column(v))
@_try_remote_functions
def schema_of_variant_agg(v: "ColumnOrName") -> Column:
"""
Returns the merged schema in the SQL format of a variant column.
.. versionadded:: 4.0.0
Parameters
----------
v : :class:`~pyspark.sql.Column` or str
a variant column or column name
Returns
-------
:class:`~pyspark.sql.Column`
a string column representing the variant schema
Examples
--------
>>> df = spark.createDataFrame([ {'json': '''{ "a" : 1 }'''} ])
>>> df.select(schema_of_variant_agg(parse_json(df.json)).alias("r")).collect()
[Row(r='OBJECT<a: BIGINT>')]
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("schema_of_variant_agg", _to_java_column(v))
@_try_remote_functions
def to_json(col: "ColumnOrName", options: Optional[Mapping[str, str]] = None) -> Column:
"""
Converts a column containing a :class:`StructType`, :class:`ArrayType`, :class:`MapType`
or a :class:`VariantType` into a JSON string. Throws an exception, in the case of an unsupported type.
.. versionadded:: 2.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column containing a struct, an array, a map, or a variant object.
options : dict, optional
options to control converting. accepts the same options as the JSON datasource.
See `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option>`_
for the version you use.
Additionally the function supports the `pretty` option which enables
pretty JSON generation.
.. # noqa
Returns
-------
:class:`~pyspark.sql.Column`
JSON object as string column.
Examples
--------
Example 1: Converting a StructType column to JSON
>>> import pyspark.sql.functions as sf
>>> from pyspark.sql import Row
>>> data = [(1, Row(age=2, name='Alice'))]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(sf.to_json(df.value).alias("json")).show(truncate=False)
+------------------------+
|json |
+------------------------+
|{"age":2,"name":"Alice"}|
+------------------------+
Example 2: Converting an ArrayType column to JSON
>>> import pyspark.sql.functions as sf
>>> from pyspark.sql import Row
>>> data = [(1, [Row(age=2, name='Alice'), Row(age=3, name='Bob')])]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(sf.to_json(df.value).alias("json")).show(truncate=False)
+-------------------------------------------------+
|json |
+-------------------------------------------------+
|[{"age":2,"name":"Alice"},{"age":3,"name":"Bob"}]|
+-------------------------------------------------+
Example 3: Converting a MapType column to JSON
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1, {"name": "Alice"})], ("key", "value"))
>>> df.select(sf.to_json(df.value).alias("json")).show(truncate=False)
+----------------+
|json |
+----------------+
|{"name":"Alice"}|
+----------------+
Example 4: Converting a VariantType column to JSON
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1, '{"name": "Alice"}')], ("key", "value"))
>>> df.select(sf.to_json(sf.parse_json(df.value)).alias("json")).show(truncate=False)
+----------------+
|json |
+----------------+
|{"name":"Alice"}|
+----------------+
Example 5: Converting a nested MapType column to JSON
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1, [{"name": "Alice"}, {"name": "Bob"}])], ("key", "value"))
>>> df.select(sf.to_json(df.value).alias("json")).show(truncate=False)
+---------------------------------+
|json |
+---------------------------------+
|[{"name":"Alice"},{"name":"Bob"}]|
+---------------------------------+
Example 6: Converting a simple ArrayType column to JSON
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(1, ["Alice", "Bob"])], ("key", "value"))
>>> df.select(sf.to_json(df.value).alias("json")).show(truncate=False)
+---------------+
|json |
+---------------+
|["Alice","Bob"]|
+---------------+
Example 7: Converting to JSON with specified options
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT (DATE('2022-02-22'), 1) AS date")
>>> json1 = sf.to_json(df.date)
>>> json2 = sf.to_json(df.date, {"dateFormat": "yyyy/MM/dd"})
>>> df.select("date", json1, json2).show(truncate=False)
+---------------+------------------------------+------------------------------+
|date |to_json(date) |to_json(date) |
+---------------+------------------------------+------------------------------+
|{2022-02-22, 1}|{"col1":"2022-02-22","col2":1}|{"col1":"2022/02/22","col2":1}|
+---------------+------------------------------+------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("to_json", _to_java_column(col), _options_to_str(options))
@_try_remote_functions
def schema_of_json(json: Union[Column, str], options: Optional[Mapping[str, str]] = None) -> Column:
"""
Parses a JSON string and infers its schema in DDL format.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
json : :class:`~pyspark.sql.Column` or str
a JSON string or a foldable string column containing a JSON string.
options : dict, optional
options to control parsing. accepts the same options as the JSON datasource.
See `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option>`_
for the version you use.
.. # noqa
.. versionchanged:: 3.0.0
It accepts `options` parameter to control schema inferring.
Returns
-------
:class:`~pyspark.sql.Column`
a string representation of a :class:`StructType` parsed from given JSON.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> parsed1 = sf.schema_of_json(sf.lit('{"a": 0}'))
>>> parsed2 = sf.schema_of_json('{a: 1}', {'allowUnquotedFieldNames':'true'})
>>> spark.range(1).select(parsed1, parsed2).show()
+------------------------+----------------------+
|schema_of_json({"a": 0})|schema_of_json({a: 1})|
+------------------------+----------------------+
| STRUCT<a: BIGINT>| STRUCT<a: BIGINT>|
+------------------------+----------------------+
"""
from pyspark.sql.classic.column import _to_java_column
json = _enum_to_value(json)
if not isinstance(json, (str, Column)):
raise PySparkTypeError(
errorClass="NOT_COLUMN_OR_STR",
messageParameters={"arg_name": "json", "arg_type": type(json).__name__},
)
return _invoke_function("schema_of_json", _to_java_column(lit(json)), _options_to_str(options))
@_try_remote_functions
def json_array_length(col: "ColumnOrName") -> Column:
"""
Returns the number of elements in the outermost JSON array. `NULL` is returned in case of
any other valid JSON string, `NULL` or an invalid JSON.
.. versionadded:: 3.5.0
Parameters
----------
col: :class:`~pyspark.sql.Column` or str
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
length of json array.
Examples
--------
>>> df = spark.createDataFrame([(None,), ('[1, 2, 3]',), ('[]',)], ['data'])
>>> df.select(json_array_length(df.data).alias('r')).collect()
[Row(r=None), Row(r=3), Row(r=0)]
"""
return _invoke_function_over_columns("json_array_length", col)
@_try_remote_functions
def json_object_keys(col: "ColumnOrName") -> Column:
"""
Returns all the keys of the outermost JSON object as an array. If a valid JSON object is
given, all the keys of the outermost object will be returned as an array. If it is any
other valid JSON string, an invalid JSON string or an empty string, the function returns null.
.. versionadded:: 3.5.0
Parameters
----------
col: :class:`~pyspark.sql.Column` or str
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
all the keys of the outermost JSON object.
Examples
--------
>>> df = spark.createDataFrame([(None,), ('{}',), ('{"key1":1, "key2":2}',)], ['data'])
>>> df.select(json_object_keys(df.data).alias('r')).collect()
[Row(r=None), Row(r=[]), Row(r=['key1', 'key2'])]
"""
return _invoke_function_over_columns("json_object_keys", col)
# TODO: Fix and add an example for StructType with Spark Connect
# e.g., StructType([StructField("a", IntegerType())])
@_try_remote_functions
def from_xml(
col: "ColumnOrName",
schema: Union[StructType, Column, str],
options: Optional[Mapping[str, str]] = None,
) -> Column:
"""
Parses a column containing a XML string to a row with
the specified schema. Returns `null`, in the case of an unparsable string.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
a column or column name in XML format
schema : :class:`StructType`, :class:`~pyspark.sql.Column` or str
a StructType, Column or Python string literal with a DDL-formatted string
to use when parsing the Xml column
options : dict, optional
options to control parsing. accepts the same options as the Xml datasource.
See `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-xml.html#data-source-option>`_
for the version you use.
.. # noqa
Returns
-------
:class:`~pyspark.sql.Column`
a new column of complex type from given XML object.
Examples
--------
Example 1: Parsing XML with a DDL-formatted string schema
>>> import pyspark.sql.functions as sf
>>> data = [(1, '''<p><a>1</a></p>''')]
>>> df = spark.createDataFrame(data, ("key", "value"))
... # Define the schema using a DDL-formatted string
>>> schema = "STRUCT<a: BIGINT>"
... # Parse the XML column using the DDL-formatted schema
>>> df.select(sf.from_xml(df.value, schema).alias("xml")).collect()
[Row(xml=Row(a=1))]
Example 2: Parsing XML with a :class:`StructType` schema
>>> import pyspark.sql.functions as sf
>>> from pyspark.sql.types import StructType, LongType
>>> data = [(1, '''<p><a>1</a></p>''')]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> schema = StructType().add("a", LongType())
>>> df.select(sf.from_xml(df.value, schema)).show()
+---------------+
|from_xml(value)|
+---------------+
| {1}|
+---------------+
Example 3: Parsing XML with :class:`ArrayType` in schema
>>> import pyspark.sql.functions as sf
>>> data = [(1, '<p><a>1</a><a>2</a></p>')]
>>> df = spark.createDataFrame(data, ("key", "value"))
... # Define the schema with an Array type
>>> schema = "STRUCT<a: ARRAY<BIGINT>>"
... # Parse the XML column using the schema with an Array
>>> df.select(sf.from_xml(df.value, schema).alias("xml")).collect()
[Row(xml=Row(a=[1, 2]))]
Example 4: Parsing XML using :meth:`pyspark.sql.functions.schema_of_xml`
>>> import pyspark.sql.functions as sf
>>> # Sample data with an XML column
... data = [(1, '<p><a>1</a><a>2</a></p>')]
>>> df = spark.createDataFrame(data, ("key", "value"))
... # Generate the schema from an example XML value
>>> schema = sf.schema_of_xml(sf.lit(data[0][1]))
... # Parse the XML column using the generated schema
>>> df.select(sf.from_xml(df.value, schema).alias("xml")).collect()
[Row(xml=Row(a=[1, 2]))]
"""
from pyspark.sql.classic.column import _to_java_column
if isinstance(schema, StructType):
schema = schema.json()
elif isinstance(schema, Column):
schema = _to_java_column(schema)
elif not isinstance(schema, str):
raise PySparkTypeError(
errorClass="NOT_COLUMN_OR_STR_OR_STRUCT",
messageParameters={"arg_name": "schema", "arg_type": type(schema).__name__},
)
return _invoke_function("from_xml", _to_java_column(col), schema, _options_to_str(options))
@_try_remote_functions
def schema_of_xml(xml: Union[Column, str], options: Optional[Mapping[str, str]] = None) -> Column:
"""
Parses a XML string and infers its schema in DDL format.
.. versionadded:: 4.0.0
Parameters
----------
xml : :class:`~pyspark.sql.Column` or str
a XML string or a foldable string column containing a XML string.
options : dict, optional
options to control parsing. accepts the same options as the XML datasource.
See `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-xml.html#data-source-option>`_
for the version you use.
.. # noqa
Returns
-------
:class:`~pyspark.sql.Column`
a string representation of a :class:`StructType` parsed from given XML.
Examples
--------
Example 1: Parsing a simple XML with a single element
>>> from pyspark.sql import functions as sf
>>> df = spark.range(1)
>>> df.select(sf.schema_of_xml(sf.lit('<p><a>1</a></p>')).alias("xml")).collect()
[Row(xml='STRUCT<a: BIGINT>')]
Example 2: Parsing an XML with multiple elements in an array
>>> from pyspark.sql import functions as sf
>>> df.select(sf.schema_of_xml(sf.lit('<p><a>1</a><a>2</a></p>')).alias("xml")).collect()
[Row(xml='STRUCT<a: ARRAY<BIGINT>>')]
Example 3: Parsing XML with options to exclude attributes
>>> from pyspark.sql import functions as sf
>>> schema = sf.schema_of_xml('<p><a attr="2">1</a></p>', {'excludeAttribute':'true'})
>>> df.select(schema.alias("xml")).collect()
[Row(xml='STRUCT<a: BIGINT>')]
Example 4: Parsing XML with complex structure
>>> from pyspark.sql import functions as sf
>>> df.select(
... sf.schema_of_xml(
... sf.lit('<root><person><name>Alice</name><age>30</age></person></root>')
... ).alias("xml")
... ).collect()
[Row(xml='STRUCT<person: STRUCT<age: BIGINT, name: STRING>>')]
Example 5: Parsing XML with nested arrays
>>> from pyspark.sql import functions as sf
>>> df.select(
... sf.schema_of_xml(
... sf.lit('<data><values><value>1</value><value>2</value></values></data>')
... ).alias("xml")
... ).collect()
[Row(xml='STRUCT<values: STRUCT<value: ARRAY<BIGINT>>>')]
"""
from pyspark.sql.classic.column import _to_java_column
xml = _enum_to_value(xml)
if not isinstance(xml, (str, Column)):
raise PySparkTypeError(
errorClass="NOT_COLUMN_OR_STR",
messageParameters={"arg_name": "xml", "arg_type": type(xml).__name__},
)
return _invoke_function("schema_of_xml", _to_java_column(lit(xml)), _options_to_str(options))
@_try_remote_functions
def to_xml(col: "ColumnOrName", options: Optional[Mapping[str, str]] = None) -> Column:
"""
Converts a column containing a :class:`StructType` into a XML string.
Throws an exception, in the case of an unsupported type.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column containing a struct.
options: dict, optional
options to control converting. accepts the same options as the XML datasource.
See `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-xml.html#data-source-option>`_
for the version you use.
.. # noqa
Returns
-------
:class:`~pyspark.sql.Column`
a XML string converted from given :class:`StructType`.
Examples
--------
>>> from pyspark.sql import Row
>>> data = [(1, Row(age=2, name='Alice'))]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_xml(df.value, {'rowTag':'person'}).alias("xml")).collect()
[Row(xml='<person>\\n <age>2</age>\\n <name>Alice</name>\\n</person>')]
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("to_xml", _to_java_column(col), _options_to_str(options))
@_try_remote_functions
def schema_of_csv(csv: Union[Column, str], options: Optional[Mapping[str, str]] = None) -> Column:
"""
CSV Function: Parses a CSV string and infers its schema in DDL format.
.. versionadded:: 3.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
csv : :class:`~pyspark.sql.Column` or str
A CSV string or a foldable string column containing a CSV string.
options : dict, optional
Options to control parsing. Accepts the same options as the CSV datasource.
See `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option>`_
for the version you use.
.. # noqa
Returns
-------
:class:`~pyspark.sql.Column`
A string representation of a :class:`StructType` parsed from the given CSV.
Examples
--------
Example 1: Inferring the schema of a CSV string with different data types
>>> from pyspark.sql import functions as sf
>>> df = spark.range(1)
>>> df.select(sf.schema_of_csv(sf.lit('1|a|true'), {'sep':'|'})).show(truncate=False)
+-------------------------------------------+
|schema_of_csv(1|a|true) |
+-------------------------------------------+
|STRUCT<_c0: INT, _c1: STRING, _c2: BOOLEAN>|
+-------------------------------------------+
Example 2: Inferring the schema of a CSV string with missing values
>>> from pyspark.sql import functions as sf
>>> df = spark.range(1)
>>> df.select(sf.schema_of_csv(sf.lit('1||true'), {'sep':'|'})).show(truncate=False)
+-------------------------------------------+
|schema_of_csv(1||true) |
+-------------------------------------------+
|STRUCT<_c0: INT, _c1: STRING, _c2: BOOLEAN>|
+-------------------------------------------+
Example 3: Inferring the schema of a CSV string with a different delimiter
>>> from pyspark.sql import functions as sf
>>> df = spark.range(1)
>>> df.select(sf.schema_of_csv(sf.lit('1;a;true'), {'sep':';'})).show(truncate=False)
+-------------------------------------------+
|schema_of_csv(1;a;true) |
+-------------------------------------------+
|STRUCT<_c0: INT, _c1: STRING, _c2: BOOLEAN>|
+-------------------------------------------+
Example 4: Inferring the schema of a CSV string with quoted fields
>>> from pyspark.sql import functions as sf
>>> df = spark.range(1)
>>> df.select(sf.schema_of_csv(sf.lit('"1","a","true"'), {'sep':','})).show(truncate=False)
+-------------------------------------------+
|schema_of_csv("1","a","true") |
+-------------------------------------------+
|STRUCT<_c0: INT, _c1: STRING, _c2: BOOLEAN>|
+-------------------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
csv = _enum_to_value(csv)
if not isinstance(csv, (str, Column)):
raise PySparkTypeError(
errorClass="NOT_COLUMN_OR_STR",
messageParameters={"arg_name": "csv", "arg_type": type(csv).__name__},
)
return _invoke_function("schema_of_csv", _to_java_column(lit(csv)), _options_to_str(options))
@_try_remote_functions
def to_csv(col: "ColumnOrName", options: Optional[Mapping[str, str]] = None) -> Column:
"""
CSV Function: Converts a column containing a :class:`StructType` into a CSV string.
Throws an exception, in the case of an unsupported type.
.. versionadded:: 3.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Name of column containing a struct.
options: dict, optional
Options to control converting. Accepts the same options as the CSV datasource.
See `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option>`_
for the version you use.
.. # noqa
Returns
-------
:class:`~pyspark.sql.Column`
A CSV string converted from the given :class:`StructType`.
Examples
--------
Example 1: Converting a simple StructType to a CSV string
>>> from pyspark.sql import Row, functions as sf
>>> data = [(1, Row(age=2, name='Alice'))]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(sf.to_csv(df.value)).show()
+-------------+
|to_csv(value)|
+-------------+
| 2,Alice|
+-------------+
Example 2: Converting a complex StructType to a CSV string
>>> from pyspark.sql import Row, functions as sf
>>> data = [(1, Row(age=2, name='Alice', scores=[100, 200, 300]))]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(sf.to_csv(df.value)).show(truncate=False)
+-------------------------+
|to_csv(value) |
+-------------------------+
|2,Alice,"[100, 200, 300]"|
+-------------------------+
Example 3: Converting a StructType with null values to a CSV string
>>> from pyspark.sql import Row, functions as sf
>>> from pyspark.sql.types import StructType, StructField, IntegerType, StringType
>>> data = [(1, Row(age=None, name='Alice'))]
>>> schema = StructType([
... StructField("key", IntegerType(), True),
... StructField("value", StructType([
... StructField("age", IntegerType(), True),
... StructField("name", StringType(), True)
... ]), True)
... ])
>>> df = spark.createDataFrame(data, schema)
>>> df.select(sf.to_csv(df.value)).show()
+-------------+
|to_csv(value)|
+-------------+
| ,Alice|
+-------------+
Example 4: Converting a StructType with different data types to a CSV string
>>> from pyspark.sql import Row, functions as sf
>>> data = [(1, Row(age=2, name='Alice', isStudent=True))]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(sf.to_csv(df.value)).show()
+-------------+
|to_csv(value)|
+-------------+
| 2,Alice,true|
+-------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("to_csv", _to_java_column(col), _options_to_str(options))
@_try_remote_functions
def size(col: "ColumnOrName") -> Column:
"""
Collection function: returns the length of the array or map stored in the column.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
Returns
-------
:class:`~pyspark.sql.Column`
length of the array/map.
Examples
--------
>>> df = spark.createDataFrame([([1, 2, 3],),([1],),([],)], ['data'])
>>> df.select(size(df.data)).collect()
[Row(size(data)=3), Row(size(data)=1), Row(size(data)=0)]
"""
return _invoke_function_over_columns("size", col)
@_try_remote_functions
def array_min(col: "ColumnOrName") -> Column:
"""
Array function: returns the minimum value of the array.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The name of the column or an expression that represents the array.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains the minimum value of each array.
Examples
--------
Example 1: Basic usage with integer array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([2, 1, 3],), ([None, 10, -1],)], ['data'])
>>> df.select(sf.array_min(df.data)).show()
+---------------+
|array_min(data)|
+---------------+
| 1|
| -1|
+---------------+
Example 2: Usage with string array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['apple', 'banana', 'cherry'],)], ['data'])
>>> df.select(sf.array_min(df.data)).show()
+---------------+
|array_min(data)|
+---------------+
| apple|
+---------------+
Example 3: Usage with mixed type array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['apple', 1, 'cherry'],)], ['data'])
>>> df.select(sf.array_min(df.data)).show()
+---------------+
|array_min(data)|
+---------------+
| 1|
+---------------+
Example 4: Usage with array of arrays
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([[2, 1], [3, 4]],)], ['data'])
>>> df.select(sf.array_min(df.data)).show()
+---------------+
|array_min(data)|
+---------------+
| [2, 1]|
+---------------+
Example 5: Usage with empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", ArrayType(IntegerType()), True)
... ])
>>> df = spark.createDataFrame([([],)], schema=schema)
>>> df.select(sf.array_min(df.data)).show()
+---------------+
|array_min(data)|
+---------------+
| NULL|
+---------------+
"""
return _invoke_function_over_columns("array_min", col)
@_try_remote_functions
def array_max(col: "ColumnOrName") -> Column:
"""
Array function: returns the maximum value of the array.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The name of the column or an expression that represents the array.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains the maximum value of each array.
Examples
--------
Example 1: Basic usage with integer array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([2, 1, 3],), ([None, 10, -1],)], ['data'])
>>> df.select(sf.array_max(df.data)).show()
+---------------+
|array_max(data)|
+---------------+
| 3|
| 10|
+---------------+
Example 2: Usage with string array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['apple', 'banana', 'cherry'],)], ['data'])
>>> df.select(sf.array_max(df.data)).show()
+---------------+
|array_max(data)|
+---------------+
| cherry|
+---------------+
Example 3: Usage with mixed type array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['apple', 1, 'cherry'],)], ['data'])
>>> df.select(sf.array_max(df.data)).show()
+---------------+
|array_max(data)|
+---------------+
| cherry|
+---------------+
Example 4: Usage with array of arrays
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([[2, 1], [3, 4]],)], ['data'])
>>> df.select(sf.array_max(df.data)).show()
+---------------+
|array_max(data)|
+---------------+
| [3, 4]|
+---------------+
Example 5: Usage with empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", ArrayType(IntegerType()), True)
... ])
>>> df = spark.createDataFrame([([],)], schema=schema)
>>> df.select(sf.array_max(df.data)).show()
+---------------+
|array_max(data)|
+---------------+
| NULL|
+---------------+
"""
return _invoke_function_over_columns("array_max", col)
@_try_remote_functions
def array_size(col: "ColumnOrName") -> Column:
"""
Array function: returns the total number of elements in the array.
The function returns null for null input.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The name of the column or an expression that represents the array.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains the size of each array.
Examples
--------
Example 1: Basic usage with integer array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([2, 1, 3],), (None,)], ['data'])
>>> df.select(sf.array_size(df.data)).show()
+----------------+
|array_size(data)|
+----------------+
| 3|
| NULL|
+----------------+
Example 2: Usage with string array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['apple', 'banana', 'cherry'],)], ['data'])
>>> df.select(sf.array_size(df.data)).show()
+----------------+
|array_size(data)|
+----------------+
| 3|
+----------------+
Example 3: Usage with mixed type array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['apple', 1, 'cherry'],)], ['data'])
>>> df.select(sf.array_size(df.data)).show()
+----------------+
|array_size(data)|
+----------------+
| 3|
+----------------+
Example 4: Usage with array of arrays
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([[2, 1], [3, 4]],)], ['data'])
>>> df.select(sf.array_size(df.data)).show()
+----------------+
|array_size(data)|
+----------------+
| 2|
+----------------+
Example 5: Usage with empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", ArrayType(IntegerType()), True)
... ])
>>> df = spark.createDataFrame([([],)], schema=schema)
>>> df.select(sf.array_size(df.data)).show()
+----------------+
|array_size(data)|
+----------------+
| 0|
+----------------+
"""
return _invoke_function_over_columns("array_size", col)
@_try_remote_functions
def cardinality(col: "ColumnOrName") -> Column:
"""
Collection function: returns the length of the array or map stored in the column.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
target column to compute on.
Returns
-------
:class:`~pyspark.sql.Column`
length of the array/map.
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.createDataFrame(
... [([1, 2, 3],),([1],),([],)], ['data']
... ).select(sf.cardinality("data")).show()
+-----------------+
|cardinality(data)|
+-----------------+
| 3|
| 1|
| 0|
+-----------------+
"""
return _invoke_function_over_columns("cardinality", col)
@_try_remote_functions
def sort_array(col: "ColumnOrName", asc: bool = True) -> Column:
"""
Array function: Sorts the input array in ascending or descending order according
to the natural ordering of the array elements. Null elements will be placed at the beginning
of the returned array in ascending order or at the end of the returned array in descending
order.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Name of the column or expression.
asc : bool, optional
Whether to sort in ascending or descending order. If `asc` is True (default),
then the sorting is in ascending order. If False, then in descending order.
Returns
-------
:class:`~pyspark.sql.Column`
Sorted array.
Examples
--------
Example 1: Sorting an array in ascending order
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([([2, 1, None, 3],)], ['data'])
>>> df.select(sf.sort_array(df.data)).show()
+----------------------+
|sort_array(data, true)|
+----------------------+
| [NULL, 1, 2, 3]|
+----------------------+
Example 2: Sorting an array in descending order
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([([2, 1, None, 3],)], ['data'])
>>> df.select(sf.sort_array(df.data, asc=False)).show()
+-----------------------+
|sort_array(data, false)|
+-----------------------+
| [3, 2, 1, NULL]|
+-----------------------+
Example 3: Sorting an array with a single element
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([([1],)], ['data'])
>>> df.select(sf.sort_array(df.data)).show()
+----------------------+
|sort_array(data, true)|
+----------------------+
| [1]|
+----------------------+
Example 4: Sorting an empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, StringType, StructField, StructType
>>> schema = StructType([StructField("data", ArrayType(StringType()), True)])
>>> df = spark.createDataFrame([([],)], schema=schema)
>>> df.select(sf.sort_array(df.data)).show()
+----------------------+
|sort_array(data, true)|
+----------------------+
| []|
+----------------------+
Example 5: Sorting an array with null values
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, IntegerType, StructType, StructField
>>> schema = StructType([StructField("data", ArrayType(IntegerType()), True)])
>>> df = spark.createDataFrame([([None, None, None],)], schema=schema)
>>> df.select(sf.sort_array(df.data)).show()
+----------------------+
|sort_array(data, true)|
+----------------------+
| [NULL, NULL, NULL]|
+----------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("sort_array", _to_java_column(col), _enum_to_value(asc))
@_try_remote_functions
def array_sort(
col: "ColumnOrName", comparator: Optional[Callable[[Column, Column], Column]] = None
) -> Column:
"""
Collection function: sorts the input array in ascending order. The elements of the input array
must be orderable. Null elements will be placed at the end of the returned array.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Can take a `comparator` function.
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
comparator : callable, optional
A binary ``(Column, Column) -> Column: ...``.
The comparator will take two
arguments representing two elements of the array. It returns a negative integer, 0, or a
positive integer as the first element is less than, equal to, or greater than the second
element. If the comparator function returns null, the function will fail and raise an error.
Returns
-------
:class:`~pyspark.sql.Column`
sorted array.
Examples
--------
>>> df = spark.createDataFrame([([2, 1, None, 3],),([1],),([],)], ['data'])
>>> df.select(array_sort(df.data).alias('r')).collect()
[Row(r=[1, 2, 3, None]), Row(r=[1]), Row(r=[])]
>>> df = spark.createDataFrame([(["foo", "foobar", None, "bar"],),(["foo"],),([],)], ['data'])
>>> df.select(array_sort(
... "data",
... lambda x, y: when(x.isNull() | y.isNull(), lit(0)).otherwise(length(y) - length(x))
... ).alias("r")).collect()
[Row(r=['foobar', 'foo', None, 'bar']), Row(r=['foo']), Row(r=[])]
"""
if comparator is None:
return _invoke_function_over_columns("array_sort", col)
else:
return _invoke_higher_order_function("array_sort", [col], [comparator])
@_try_remote_functions
def shuffle(col: "ColumnOrName", seed: Optional[Union[Column, int]] = None) -> Column:
"""
Array function: Generates a random permutation of the given array.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The name of the column or expression to be shuffled.
seed : :class:`~pyspark.sql.Column` or int, optional
Seed value for the random generator.
.. versionadded:: 4.0.0
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains an array of elements in random order.
Notes
-----
The `shuffle` function is non-deterministic, meaning the order of the output array
can be different for each execution.
Examples
--------
Example 1: Shuffling a simple array
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT ARRAY(1, 20, 3, 5) AS data")
>>> df.select("*", sf.shuffle(df.data, sf.lit(123))).show()
+-------------+-------------+
| data|shuffle(data)|
+-------------+-------------+
|[1, 20, 3, 5]|[5, 1, 20, 3]|
+-------------+-------------+
Example 2: Shuffling an array with null values
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT ARRAY(1, 20, NULL, 5) AS data")
>>> df.select("*", sf.shuffle(sf.col("data"), 234)).show()
+----------------+----------------+
| data| shuffle(data)|
+----------------+----------------+
|[1, 20, NULL, 5]|[NULL, 5, 20, 1]|
+----------------+----------------+
Example 3: Shuffling an array with duplicate values
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT ARRAY(1, 2, 2, 3, 3, 3) AS data")
>>> df.select("*", sf.shuffle("data", 345)).show()
+------------------+------------------+
| data| shuffle(data)|
+------------------+------------------+
|[1, 2, 2, 3, 3, 3]|[2, 3, 3, 1, 2, 3]|
+------------------+------------------+
Example 4: Shuffling an array with random seed
>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT ARRAY(1, 2, 2, 3, 3, 3) AS data")
>>> df.select("*", sf.shuffle("data")).show() # doctest: +SKIP
+------------------+------------------+
| data| shuffle(data)|
+------------------+------------------+
|[1, 2, 2, 3, 3, 3]|[3, 3, 2, 3, 2, 1]|
+------------------+------------------+
"""
if seed is not None:
return _invoke_function_over_columns("shuffle", col, lit(seed))
else:
return _invoke_function_over_columns("shuffle", col)
@_try_remote_functions
def reverse(col: "ColumnOrName") -> Column:
"""
Collection function: returns a reversed string or an array with elements in reverse order.
.. versionadded:: 1.5.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The name of the column or an expression that represents the element to be reversed.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a reversed string or an array with elements in reverse order.
Examples
--------
Example 1: Reverse a string
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('Spark SQL',)], ['data'])
>>> df.select(sf.reverse(df.data)).show()
+-------------+
|reverse(data)|
+-------------+
| LQS krapS|
+-------------+
Example 2: Reverse an array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([2, 1, 3],) ,([1],) ,([],)], ['data'])
>>> df.select(sf.reverse(df.data)).show()
+-------------+
|reverse(data)|
+-------------+
| [3, 1, 2]|
| [1]|
| []|
+-------------+
"""
return _invoke_function_over_columns("reverse", col)
@_try_remote_functions
def flatten(col: "ColumnOrName") -> Column:
"""
Array function: creates a single array from an array of arrays.
If a structure of nested arrays is deeper than two levels,
only one level of nesting is removed.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The name of the column or expression to be flattened.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains the flattened array.
Examples
--------
Example 1: Flattening a simple nested array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([[1, 2, 3], [4, 5], [6]],)], ['data'])
>>> df.select(sf.flatten(df.data)).show()
+------------------+
| flatten(data)|
+------------------+
|[1, 2, 3, 4, 5, 6]|
+------------------+
Example 2: Flattening an array with null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([None, [4, 5]],)], ['data'])
>>> df.select(sf.flatten(df.data)).show()
+-------------+
|flatten(data)|
+-------------+
| NULL|
+-------------+
Example 3: Flattening an array with more than two levels of nesting
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([[[1, 2], [3, 4]], [[5, 6], [7, 8]]],)], ['data'])
>>> df.select(sf.flatten(df.data)).show(truncate=False)
+--------------------------------+
|flatten(data) |
+--------------------------------+
|[[1, 2], [3, 4], [5, 6], [7, 8]]|
+--------------------------------+
Example 4: Flattening an array with mixed types
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([['a', 'b', 'c'], [1, 2, 3]],)], ['data'])
>>> df.select(sf.flatten(df.data)).show()
+------------------+
| flatten(data)|
+------------------+
|[a, b, c, 1, 2, 3]|
+------------------+
"""
return _invoke_function_over_columns("flatten", col)
@_try_remote_functions
def map_contains_key(col: "ColumnOrName", value: Any) -> Column:
"""
Map function: Returns true if the map contains the key.
.. versionadded:: 3.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The name of the column or an expression that represents the map.
value :
A literal value, or a :class:`~pyspark.sql.Column` expression.
.. versionchanged:: 4.0.0
`value` now also accepts a Column type.
Returns
-------
:class:`~pyspark.sql.Column`
True if key is in the map and False otherwise.
Examples
--------
Example 1: The key is in the map
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data")
>>> df.select(sf.map_contains_key("data", 1)).show()
+-------------------------+
|map_contains_key(data, 1)|
+-------------------------+
| true|
+-------------------------+
Example 2: The key is not in the map
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data")
>>> df.select(sf.map_contains_key("data", -1)).show()
+--------------------------+
|map_contains_key(data, -1)|
+--------------------------+
| false|
+--------------------------+
Example 3: Check for key using a column
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data, 1 as key")
>>> df.select(sf.map_contains_key("data", sf.col("key"))).show()
+---------------------------+
|map_contains_key(data, key)|
+---------------------------+
| true|
+---------------------------+
"""
return _invoke_function_over_columns("map_contains_key", col, lit(value))
@_try_remote_functions
def map_keys(col: "ColumnOrName") -> Column:
"""
Map function: Returns an unordered array containing the keys of the map.
.. versionadded:: 2.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Name of column or expression
Returns
-------
:class:`~pyspark.sql.Column`
Keys of the map as an array.
Examples
--------
Example 1: Extracting keys from a simple map
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data")
>>> df.select(sf.sort_array(sf.map_keys("data"))).show()
+--------------------------------+
|sort_array(map_keys(data), true)|
+--------------------------------+
| [1, 2]|
+--------------------------------+
Example 2: Extracting keys from a map with complex keys
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(array(1, 2), 'a', array(3, 4), 'b') as data")
>>> df.select(sf.sort_array(sf.map_keys("data"))).show()
+--------------------------------+
|sort_array(map_keys(data), true)|
+--------------------------------+
| [[1, 2], [3, 4]]|
+--------------------------------+
Example 3: Extracting keys from a map with duplicate keys
>>> from pyspark.sql import functions as sf
>>> originalmapKeyDedupPolicy = spark.conf.get("spark.sql.mapKeyDedupPolicy")
>>> spark.conf.set("spark.sql.mapKeyDedupPolicy", "LAST_WIN")
>>> df = spark.sql("SELECT map(1, 'a', 1, 'b') as data")
>>> df.select(sf.map_keys("data")).show()
+--------------+
|map_keys(data)|
+--------------+
| [1]|
+--------------+
>>> spark.conf.set("spark.sql.mapKeyDedupPolicy", originalmapKeyDedupPolicy)
Example 4: Extracting keys from an empty map
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map() as data")
>>> df.select(sf.map_keys("data")).show()
+--------------+
|map_keys(data)|
+--------------+
| []|
+--------------+
"""
return _invoke_function_over_columns("map_keys", col)
@_try_remote_functions
def map_values(col: "ColumnOrName") -> Column:
"""
Map function: Returns an unordered array containing the values of the map.
.. versionadded:: 2.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Name of column or expression
Returns
-------
:class:`~pyspark.sql.Column`
Values of the map as an array.
Examples
--------
Example 1: Extracting values from a simple map
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data")
>>> df.select(sf.sort_array(sf.map_values("data"))).show()
+----------------------------------+
|sort_array(map_values(data), true)|
+----------------------------------+
| [a, b]|
+----------------------------------+
Example 2: Extracting values from a map with complex values
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, array('a', 'b'), 2, array('c', 'd')) as data")
>>> df.select(sf.sort_array(sf.map_values("data"))).show()
+----------------------------------+
|sort_array(map_values(data), true)|
+----------------------------------+
| [[a, b], [c, d]]|
+----------------------------------+
Example 3: Extracting values from a map with null values
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, null, 2, 'b') as data")
>>> df.select(sf.sort_array(sf.map_values("data"))).show()
+----------------------------------+
|sort_array(map_values(data), true)|
+----------------------------------+
| [NULL, b]|
+----------------------------------+
Example 4: Extracting values from a map with duplicate values
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'a') as data")
>>> df.select(sf.map_values("data")).show()
+----------------+
|map_values(data)|
+----------------+
| [a, a]|
+----------------+
Example 5: Extracting values from an empty map
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map() as data")
>>> df.select(sf.map_values("data")).show()
+----------------+
|map_values(data)|
+----------------+
| []|
+----------------+
"""
return _invoke_function_over_columns("map_values", col)
@_try_remote_functions
def map_entries(col: "ColumnOrName") -> Column:
"""
Map function: Returns an unordered array of all entries in the given map.
.. versionadded:: 3.0.0
.. versionchanged:: 3.4.0
Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Name of column or expression
Returns
-------
:class:`~pyspark.sql.Column`
An array of key value pairs as a struct type
Examples
--------
Example 1: Extracting entries from a simple map
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data")
>>> df.select(sf.sort_array(sf.map_entries("data"))).show()
+-----------------------------------+
|sort_array(map_entries(data), true)|
+-----------------------------------+
| [{1, a}, {2, b}]|
+-----------------------------------+
Example 2: Extracting entries from a map with complex keys and values
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(array(1, 2), array('a', 'b'), "
... "array(3, 4), array('c', 'd')) as data")
>>> df.select(sf.sort_array(sf.map_entries("data"))).show(truncate=False)
+------------------------------------+
|sort_array(map_entries(data), true) |
+------------------------------------+
|[{[1, 2], [a, b]}, {[3, 4], [c, d]}]|
+------------------------------------+
Example 3: Extracting entries from a map with duplicate keys
>>> from pyspark.sql import functions as sf
>>> originalmapKeyDedupPolicy = spark.conf.get("spark.sql.mapKeyDedupPolicy")
>>> spark.conf.set("spark.sql.mapKeyDedupPolicy", "LAST_WIN")
>>> df = spark.sql("SELECT map(1, 'a', 1, 'b') as data")
>>> df.select(sf.map_entries("data")).show()
+-----------------+
|map_entries(data)|
+-----------------+
| [{1, b}]|
+-----------------+
>>> spark.conf.set("spark.sql.mapKeyDedupPolicy", originalmapKeyDedupPolicy)
Example 4: Extracting entries from an empty map
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map() as data")
>>> df.select(sf.map_entries("data")).show()
+-----------------+
|map_entries(data)|
+-----------------+
| []|
+-----------------+
"""
return _invoke_function_over_columns("map_entries", col)
@_try_remote_functions
def map_from_entries(col: "ColumnOrName") -> Column:
"""
Map function: Transforms an array of key-value pair entries (structs with two fields)
into a map. The first field of each entry is used as the key and the second field
as the value in the resulting map column
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
Name of column or expression
Returns
-------
:class:`~pyspark.sql.Column`
A map created from the given array of entries.
Examples
--------
Example 1: Basic usage of map_from_entries
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT array(struct(1, 'a'), struct(2, 'b')) as data")
>>> df.select(sf.map_from_entries(df.data)).show()
+----------------------+
|map_from_entries(data)|
+----------------------+
| {1 -> a, 2 -> b}|
+----------------------+
Example 2: map_from_entries with null values
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT array(struct(1, null), struct(2, 'b')) as data")
>>> df.select(sf.map_from_entries(df.data)).show()
+----------------------+
|map_from_entries(data)|
+----------------------+
| {1 -> NULL, 2 -> b}|
+----------------------+
Example 3: map_from_entries with a DataFrame
>>> from pyspark.sql import Row, functions as sf
>>> df = spark.createDataFrame([([Row(1, "a"), Row(2, "b")],), ([Row(3, "c")],)], ['data'])
>>> df.select(sf.map_from_entries(df.data)).show()
+----------------------+
|map_from_entries(data)|
+----------------------+
| {1 -> a, 2 -> b}|
| {3 -> c}|
+----------------------+
Example 4: map_from_entries with empty array
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import ArrayType, StringType, IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", ArrayType(
... StructType([
... StructField("key", IntegerType()),
... StructField("value", StringType())
... ])
... ), True)
... ])
>>> df = spark.createDataFrame([([],)], schema=schema)
>>> df.select(sf.map_from_entries(df.data)).show()
+----------------------+
|map_from_entries(data)|
+----------------------+
| {}|
+----------------------+
"""
return _invoke_function_over_columns("map_from_entries", col)
@_try_remote_functions
def array_repeat(col: "ColumnOrName", count: Union["ColumnOrName", int]) -> Column:
"""
Array function: creates an array containing a column repeated count times.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The name of the column or an expression that represents the element to be repeated.
count : :class:`~pyspark.sql.Column` or str or int
The name of the column, an expression,
or an integer that represents the number of times to repeat the element.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains an array of repeated elements.
Examples
--------
Example 1: Usage with string
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([('ab',)], ['data'])
>>> df.select(sf.array_repeat(df.data, 3)).show()
+---------------------+
|array_repeat(data, 3)|
+---------------------+
| [ab, ab, ab]|
+---------------------+
Example 2: Usage with integer
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(3,)], ['data'])
>>> df.select(sf.array_repeat(df.data, 2)).show()
+---------------------+
|array_repeat(data, 2)|
+---------------------+
| [3, 3]|
+---------------------+
Example 3: Usage with array
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(['apple', 'banana'],)], ['data'])
>>> df.select(sf.array_repeat(df.data, 2)).show(truncate=False)
+----------------------------------+
|array_repeat(data, 2) |
+----------------------------------+
|[[apple, banana], [apple, banana]]|
+----------------------------------+
Example 4: Usage with null
>>> from pyspark.sql import functions as sf
>>> from pyspark.sql.types import IntegerType, StructType, StructField
>>> schema = StructType([
... StructField("data", IntegerType(), True)
... ])
>>> df = spark.createDataFrame([(None, )], schema=schema)
>>> df.select(sf.array_repeat(df.data, 3)).show()
+---------------------+
|array_repeat(data, 3)|
+---------------------+
| [NULL, NULL, NULL]|
+---------------------+
"""
count = _enum_to_value(count)
count = lit(count) if isinstance(count, int) else count
return _invoke_function_over_columns("array_repeat", col, count)
@_try_remote_functions
def arrays_zip(*cols: "ColumnOrName") -> Column:
"""
Array function: Returns a merged array of structs in which the N-th struct contains all
N-th values of input arrays. If one of the arrays is shorter than others then
the resulting struct type value will be a `null` for missing elements.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : :class:`~pyspark.sql.Column` or str
Columns of arrays to be merged.
Returns
-------
:class:`~pyspark.sql.Column`
Merged array of entries.
Examples
--------
Example 1: Zipping two arrays of the same length
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, 3], ['a', 'b', 'c'])], ['nums', 'letters'])
>>> df.select(sf.arrays_zip(df.nums, df.letters)).show(truncate=False)
+-------------------------+
|arrays_zip(nums, letters)|
+-------------------------+
|[{1, a}, {2, b}, {3, c}] |
+-------------------------+
Example 2: Zipping arrays of different lengths
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2], ['a', 'b', 'c'])], ['nums', 'letters'])
>>> df.select(sf.arrays_zip(df.nums, df.letters)).show(truncate=False)
+---------------------------+
|arrays_zip(nums, letters) |
+---------------------------+
|[{1, a}, {2, b}, {NULL, c}]|
+---------------------------+
Example 3: Zipping more than two arrays
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame(
... [([1, 2], ['a', 'b'], [True, False])], ['nums', 'letters', 'bools'])
>>> df.select(sf.arrays_zip(df.nums, df.letters, df.bools)).show(truncate=False)
+--------------------------------+
|arrays_zip(nums, letters, bools)|
+--------------------------------+
|[{1, a, true}, {2, b, false}] |
+--------------------------------+
Example 4: Zipping arrays with null values
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([([1, 2, None], ['a', None, 'c'])], ['nums', 'letters'])
>>> df.select(sf.arrays_zip(df.nums, df.letters)).show(truncate=False)
+------------------------------+
|arrays_zip(nums, letters) |
+------------------------------+
|[{1, a}, {2, NULL}, {NULL, c}]|
+------------------------------+
"""
return _invoke_function_over_seq_of_columns("arrays_zip", cols)
@overload
def map_concat(*cols: "ColumnOrName") -> Column:
...
@overload
def map_concat(__cols: Union[Sequence["ColumnOrName"], Tuple["ColumnOrName", ...]]) -> Column:
...
@_try_remote_functions
def map_concat(
*cols: Union["ColumnOrName", Union[Sequence["ColumnOrName"], Tuple["ColumnOrName", ...]]]
) -> Column:
"""
Map function: Returns the union of all given maps.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
cols : :class:`~pyspark.sql.Column` or str
Column names or :class:`~pyspark.sql.Column`
Returns
-------
:class:`~pyspark.sql.Column`
A map of merged entries from other maps.
Notes
-----
For duplicate keys in input maps, the handling is governed by `spark.sql.mapKeyDedupPolicy`.
By default, it throws an exception. If set to `LAST_WIN`, it uses the last map's value.
Examples
--------
Example 1: Basic usage of map_concat
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as map1, map(3, 'c') as map2")
>>> df.select(sf.map_concat("map1", "map2")).show(truncate=False)
+------------------------+
|map_concat(map1, map2) |
+------------------------+
|{1 -> a, 2 -> b, 3 -> c}|
+------------------------+
Example 2: map_concat with overlapping keys
>>> from pyspark.sql import functions as sf
>>> originalmapKeyDedupPolicy = spark.conf.get("spark.sql.mapKeyDedupPolicy")
>>> spark.conf.set("spark.sql.mapKeyDedupPolicy", "LAST_WIN")
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as map1, map(2, 'c', 3, 'd') as map2")
>>> df.select(sf.map_concat("map1", "map2")).show(truncate=False)
+------------------------+
|map_concat(map1, map2) |
+------------------------+
|{1 -> a, 2 -> c, 3 -> d}|
+------------------------+
>>> spark.conf.set("spark.sql.mapKeyDedupPolicy", originalmapKeyDedupPolicy)
Example 3: map_concat with three maps
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a') as map1, map(2, 'b') as map2, map(3, 'c') as map3")
>>> df.select(sf.map_concat("map1", "map2", "map3")).show(truncate=False)
+----------------------------+
|map_concat(map1, map2, map3)|
+----------------------------+
|{1 -> a, 2 -> b, 3 -> c} |
+----------------------------+
Example 4: map_concat with empty map
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as map1, map() as map2")
>>> df.select(sf.map_concat("map1", "map2")).show(truncate=False)
+----------------------+
|map_concat(map1, map2)|
+----------------------+
|{1 -> a, 2 -> b} |
+----------------------+
Example 5: map_concat with null values
>>> from pyspark.sql import functions as sf
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as map1, map(3, null) as map2")
>>> df.select(sf.map_concat("map1", "map2")).show(truncate=False)
+---------------------------+
|map_concat(map1, map2) |
+---------------------------+
|{1 -> a, 2 -> b, 3 -> NULL}|
+---------------------------+
"""
if len(cols) == 1 and isinstance(cols[0], (list, set)):
cols = cols[0] # type: ignore[assignment]
return _invoke_function_over_seq_of_columns("map_concat", cols) # type: ignore[arg-type]
@_try_remote_functions
def sequence(
start: "ColumnOrName", stop: "ColumnOrName", step: Optional["ColumnOrName"] = None
) -> Column:
"""
Array function: Generate a sequence of integers from `start` to `stop`, incrementing by `step`.
If `step` is not set, the function increments by 1 if `start` is less than or equal to `stop`,
otherwise it decrements by 1.
.. versionadded:: 2.4.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
start : :class:`~pyspark.sql.Column` or str
The starting value (inclusive) of the sequence.
stop : :class:`~pyspark.sql.Column` or str
The last value (inclusive) of the sequence.
step : :class:`~pyspark.sql.Column` or str, optional
The value to add to the current element to get the next element in the sequence.
The default is 1 if `start` is less than or equal to `stop`, otherwise -1.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains an array of sequence values.
Examples
--------
Example 1: Generating a sequence with default step
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(-2, 2)], ['start', 'stop'])
>>> df.select(sf.sequence(df.start, df.stop)).show()
+---------------------+
|sequence(start, stop)|
+---------------------+
| [-2, -1, 0, 1, 2]|
+---------------------+
Example 2: Generating a sequence with a custom step
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(4, -4, -2)], ['start', 'stop', 'step'])
>>> df.select(sf.sequence(df.start, df.stop, df.step)).show()
+---------------------------+
|sequence(start, stop, step)|
+---------------------------+
| [4, 2, 0, -2, -4]|
+---------------------------+
Example 3: Generating a sequence with a negative step
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(5, 1, -1)], ['start', 'stop', 'step'])
>>> df.select(sf.sequence(df.start, df.stop, df.step)).show()
+---------------------------+
|sequence(start, stop, step)|
+---------------------------+
| [5, 4, 3, 2, 1]|
+---------------------------+
"""
if step is None:
return _invoke_function_over_columns("sequence", start, stop)
else:
return _invoke_function_over_columns("sequence", start, stop, step)
@_try_remote_functions
def from_csv(
col: "ColumnOrName",
schema: Union[Column, str],
options: Optional[Mapping[str, str]] = None,
) -> Column:
"""
CSV Function: Parses a column containing a CSV string into a row with the specified schema.
Returns `null` if the string cannot be parsed.
.. versionadded:: 3.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
A column or column name in CSV format.
schema : :class:`~pyspark.sql.Column` or str
A column, or Python string literal with schema in DDL format, to use when parsing the CSV column.
options : dict, optional
Options to control parsing. Accepts the same options as the CSV datasource.
See `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option>`_
for the version you use.
.. # noqa
Returns
-------
:class:`~pyspark.sql.Column`
A column of parsed CSV values.
Examples
--------
Example 1: Parsing a simple CSV string
>>> from pyspark.sql import functions as sf
>>> data = [("1,2,3",)]
>>> df = spark.createDataFrame(data, ("value",))
>>> df.select(sf.from_csv(df.value, "a INT, b INT, c INT")).show()
+---------------+
|from_csv(value)|
+---------------+
| {1, 2, 3}|
+---------------+
Example 2: Using schema_of_csv to infer the schema
>>> from pyspark.sql import functions as sf
>>> data = [("1,2,3",)]
>>> value = data[0][0]
>>> df.select(sf.from_csv(df.value, sf.schema_of_csv(value))).show()
+---------------+
|from_csv(value)|
+---------------+
| {1, 2, 3}|
+---------------+
Example 3: Ignoring leading white space in the CSV string
>>> from pyspark.sql import functions as sf
>>> data = [(" abc",)]
>>> df = spark.createDataFrame(data, ("value",))
>>> options = {'ignoreLeadingWhiteSpace': True}
>>> df.select(sf.from_csv(df.value, "s string", options)).show()
+---------------+
|from_csv(value)|
+---------------+
| {abc}|
+---------------+
Example 4: Parsing a CSV string with a missing value
>>> from pyspark.sql import functions as sf
>>> data = [("1,2,",)]
>>> df = spark.createDataFrame(data, ("value",))
>>> df.select(sf.from_csv(df.value, "a INT, b INT, c INT")).show()
+---------------+
|from_csv(value)|
+---------------+
| {1, 2, NULL}|
+---------------+
Example 5: Parsing a CSV string with a different delimiter
>>> from pyspark.sql import functions as sf
>>> data = [("1;2;3",)]
>>> df = spark.createDataFrame(data, ("value",))
>>> options = {'delimiter': ';'}
>>> df.select(sf.from_csv(df.value, "a INT, b INT, c INT", options)).show()
+---------------+
|from_csv(value)|
+---------------+
| {1, 2, 3}|
+---------------+
"""
from pyspark.sql.classic.column import _to_java_column
if not isinstance(schema, (str, Column)):
raise PySparkTypeError(
errorClass="NOT_COLUMN_OR_STR",
messageParameters={"arg_name": "schema", "arg_type": type(schema).__name__},
)
return _invoke_function(
"from_csv", _to_java_column(col), _to_java_column(lit(schema)), _options_to_str(options)
)
def _unresolved_named_lambda_variable(name: str) -> Column:
"""
Create `o.a.s.sql.expressions.UnresolvedNamedLambdaVariable`,
convert it to o.s.sql.Column and wrap in Python `Column`
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
name_parts : str
"""
from py4j.java_gateway import JVMView
sc = _get_active_spark_context()
return Column(cast(JVMView, sc._jvm).PythonSQLUtils.unresolvedNamedLambdaVariable(name))
def _get_lambda_parameters(f: Callable) -> ValuesView[inspect.Parameter]:
signature = inspect.signature(f)
parameters = signature.parameters.values()
# We should exclude functions that use
# variable args and keyword argnames
# as well as keyword only args
supported_parameter_types = {
inspect.Parameter.POSITIONAL_OR_KEYWORD,
inspect.Parameter.POSITIONAL_ONLY,
}
# Validate that
# function arity is between 1 and 3
if not (1 <= len(parameters) <= 3):
raise PySparkValueError(
errorClass="WRONG_NUM_ARGS_FOR_HIGHER_ORDER_FUNCTION",
messageParameters={"func_name": f.__name__, "num_args": str(len(parameters))},
)
# and all arguments can be used as positional
if not all(p.kind in supported_parameter_types for p in parameters):
raise PySparkValueError(
errorClass="UNSUPPORTED_PARAM_TYPE_FOR_HIGHER_ORDER_FUNCTION",
messageParameters={"func_name": f.__name__},
)
return parameters
def _create_lambda(f: Callable) -> Callable:
"""
Create `o.a.s.sql.expressions.LambdaFunction` corresponding
to transformation described by f
:param f: A Python of one of the following forms:
- (Column) -> Column: ...
- (Column, Column) -> Column: ...
- (Column, Column, Column) -> Column: ...
"""
from py4j.java_gateway import JVMView
from pyspark.sql.classic.column import _to_seq
parameters = _get_lambda_parameters(f)
sc = _get_active_spark_context()
argnames = ["x", "y", "z"]
args = [_unresolved_named_lambda_variable(arg) for arg in argnames[: len(parameters)]]
result = f(*args)
if not isinstance(result, Column):
raise PySparkValueError(
errorClass="HIGHER_ORDER_FUNCTION_SHOULD_RETURN_COLUMN",
messageParameters={"func_name": f.__name__, "return_type": type(result).__name__},
)
jexpr = result._jc
jargs = _to_seq(sc, [arg._jc for arg in args])
return cast(JVMView, sc._jvm).PythonSQLUtils.lambdaFunction(jexpr, jargs)
def _invoke_higher_order_function(
name: str,
cols: Sequence["ColumnOrName"],
funs: Sequence[Callable],
) -> Column:
"""
Invokes expression identified by name,
(relative to ```org.apache.spark.sql.catalyst.expressions``)
and wraps the result with Column (first Scala one, then Python).
:param name: Name of the expression
:param cols: a list of columns
:param funs: a list of (*Column) -> Column functions.
:return: a Column
"""
from py4j.java_gateway import JVMView
from pyspark.sql.classic.column import _to_seq, _to_java_column
sc = _get_active_spark_context()
jfuns = [_create_lambda(f) for f in funs]
jcols = [_to_java_column(c) for c in cols]
return Column(cast(JVMView, sc._jvm).PythonSQLUtils.fn(name, _to_seq(sc, jcols + jfuns)))
@overload
def transform(col: "ColumnOrName", f: Callable[[Column], Column]) -> Column:
...
@overload
def transform(col: "ColumnOrName", f: Callable[[Column, Column], Column]) -> Column:
...
@_try_remote_functions
def transform(
col: "ColumnOrName",
f: Union[Callable[[Column], Column], Callable[[Column, Column], Column]],
) -> Column:
"""
Returns an array of elements after applying a transformation to each element in the input array.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
f : function
a function that is applied to each element of the input array.
Can take one of the following forms:
- Unary ``(x: Column) -> Column: ...``
- Binary ``(x: Column, i: Column) -> Column...``, where the second argument is
a 0-based index of the element.
and can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
Returns
-------
:class:`~pyspark.sql.Column`
a new array of transformed elements.
Examples
--------
>>> df = spark.createDataFrame([(1, [1, 2, 3, 4])], ("key", "values"))
>>> df.select(transform("values", lambda x: x * 2).alias("doubled")).show()
+------------+
| doubled|
+------------+
|[2, 4, 6, 8]|
+------------+
>>> def alternate(x, i):
... return when(i % 2 == 0, x).otherwise(-x)
...
>>> df.select(transform("values", alternate).alias("alternated")).show()
+--------------+
| alternated|
+--------------+
|[1, -2, 3, -4]|
+--------------+
"""
return _invoke_higher_order_function("transform", [col], [f])
@_try_remote_functions
def exists(col: "ColumnOrName", f: Callable[[Column], Column]) -> Column:
"""
Returns whether a predicate holds for one or more elements in the array.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
f : function
``(x: Column) -> Column: ...`` returning the Boolean expression.
Can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
Returns
-------
:class:`~pyspark.sql.Column`
True if "any" element of an array evaluates to True when passed as an argument to
given function and False otherwise.
Examples
--------
>>> df = spark.createDataFrame([(1, [1, 2, 3, 4]), (2, [3, -1, 0])],("key", "values"))
>>> df.select(exists("values", lambda x: x < 0).alias("any_negative")).show()
+------------+
|any_negative|
+------------+
| false|
| true|
+------------+
"""
return _invoke_higher_order_function("exists", [col], [f])
@_try_remote_functions
def forall(col: "ColumnOrName", f: Callable[[Column], Column]) -> Column:
"""
Returns whether a predicate holds for every element in the array.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
f : function
``(x: Column) -> Column: ...`` returning the Boolean expression.
Can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
Returns
-------
:class:`~pyspark.sql.Column`
True if "all" elements of an array evaluates to True when passed as an argument to
given function and False otherwise.
Examples
--------
>>> df = spark.createDataFrame(
... [(1, ["bar"]), (2, ["foo", "bar"]), (3, ["foobar", "foo"])],
... ("key", "values")
... )
>>> df.select(forall("values", lambda x: x.rlike("foo")).alias("all_foo")).show()
+-------+
|all_foo|
+-------+
| false|
| false|
| true|
+-------+
"""
return _invoke_higher_order_function("forall", [col], [f])
@overload
def filter(col: "ColumnOrName", f: Callable[[Column], Column]) -> Column:
...
@overload
def filter(col: "ColumnOrName", f: Callable[[Column, Column], Column]) -> Column:
...
@_try_remote_functions
def filter(
col: "ColumnOrName",
f: Union[Callable[[Column], Column], Callable[[Column, Column], Column]],
) -> Column:
"""
Returns an array of elements for which a predicate holds in a given array.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
f : function
A function that returns the Boolean expression.
Can take one of the following forms:
- Unary ``(x: Column) -> Column: ...``
- Binary ``(x: Column, i: Column) -> Column...``, where the second argument is
a 0-based index of the element.
and can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
Returns
-------
:class:`~pyspark.sql.Column`
filtered array of elements where given function evaluated to True
when passed as an argument.
Examples
--------
>>> df = spark.createDataFrame(
... [(1, ["2018-09-20", "2019-02-03", "2019-07-01", "2020-06-01"])],
... ("key", "values")
... )
>>> def after_second_quarter(x):
... return month(to_date(x)) > 6
...
>>> df.select(
... filter("values", after_second_quarter).alias("after_second_quarter")
... ).show(truncate=False)
+------------------------+
|after_second_quarter |
+------------------------+
|[2018-09-20, 2019-07-01]|
+------------------------+
"""
return _invoke_higher_order_function("filter", [col], [f])
@_try_remote_functions
def aggregate(
col: "ColumnOrName",
initialValue: "ColumnOrName",
merge: Callable[[Column, Column], Column],
finish: Optional[Callable[[Column], Column]] = None,
) -> Column:
"""
Applies a binary operator to an initial state and all elements in the array,
and reduces this to a single state. The final state is converted into the final result
by applying a finish function.
Both functions can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
initialValue : :class:`~pyspark.sql.Column` or str
initial value. Name of column or expression
merge : function
a binary function ``(acc: Column, x: Column) -> Column...`` returning expression
of the same type as ``initialValue``
finish : function, optional
an optional unary function ``(x: Column) -> Column: ...``
used to convert accumulated value.
Returns
-------
:class:`~pyspark.sql.Column`
final value after aggregate function is applied.
Examples
--------
>>> df = spark.createDataFrame([(1, [20.0, 4.0, 2.0, 6.0, 10.0])], ("id", "values"))
>>> df.select(aggregate("values", lit(0.0), lambda acc, x: acc + x).alias("sum")).show()
+----+
| sum|
+----+
|42.0|
+----+
>>> def merge(acc, x):
... count = acc.count + 1
... sum = acc.sum + x
... return struct(count.alias("count"), sum.alias("sum"))
...
>>> df.select(
... aggregate(
... "values",
... struct(lit(0).alias("count"), lit(0.0).alias("sum")),
... merge,
... lambda acc: acc.sum / acc.count,
... ).alias("mean")
... ).show()
+----+
|mean|
+----+
| 8.4|
+----+
"""
if finish is not None:
return _invoke_higher_order_function("aggregate", [col, initialValue], [merge, finish])
else:
return _invoke_higher_order_function("aggregate", [col, initialValue], [merge])
@_try_remote_functions
def reduce(
col: "ColumnOrName",
initialValue: "ColumnOrName",
merge: Callable[[Column, Column], Column],
finish: Optional[Callable[[Column], Column]] = None,
) -> Column:
"""
Applies a binary operator to an initial state and all elements in the array,
and reduces this to a single state. The final state is converted into the final result
by applying a finish function.
Both functions can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
initialValue : :class:`~pyspark.sql.Column` or str
initial value. Name of column or expression
merge : function
a binary function ``(acc: Column, x: Column) -> Column...`` returning expression
of the same type as ``zero``
finish : function, optional
an optional unary function ``(x: Column) -> Column: ...``
used to convert accumulated value.
Returns
-------
:class:`~pyspark.sql.Column`
final value after aggregate function is applied.
Examples
--------
>>> df = spark.createDataFrame([(1, [20.0, 4.0, 2.0, 6.0, 10.0])], ("id", "values"))
>>> df.select(reduce("values", lit(0.0), lambda acc, x: acc + x).alias("sum")).show()
+----+
| sum|
+----+
|42.0|
+----+
>>> def merge(acc, x):
... count = acc.count + 1
... sum = acc.sum + x
... return struct(count.alias("count"), sum.alias("sum"))
...
>>> df.select(
... reduce(
... "values",
... struct(lit(0).alias("count"), lit(0.0).alias("sum")),
... merge,
... lambda acc: acc.sum / acc.count,
... ).alias("mean")
... ).show()
+----+
|mean|
+----+
| 8.4|
+----+
"""
if finish is not None:
return _invoke_higher_order_function("reduce", [col, initialValue], [merge, finish])
else:
return _invoke_higher_order_function("reduce", [col, initialValue], [merge])
@_try_remote_functions
def zip_with(
left: "ColumnOrName",
right: "ColumnOrName",
f: Callable[[Column, Column], Column],
) -> Column:
"""
Merge two given arrays, element-wise, into a single array using a function.
If one array is shorter, nulls are appended at the end to match the length of the longer
array, before applying the function.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
left : :class:`~pyspark.sql.Column` or str
name of the first column or expression
right : :class:`~pyspark.sql.Column` or str
name of the second column or expression
f : function
a binary function ``(x1: Column, x2: Column) -> Column...``
Can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
Returns
-------
:class:`~pyspark.sql.Column`
array of calculated values derived by applying given function to each pair of arguments.
Examples
--------
>>> df = spark.createDataFrame([(1, [1, 3, 5, 8], [0, 2, 4, 6])], ("id", "xs", "ys"))
>>> df.select(zip_with("xs", "ys", lambda x, y: x ** y).alias("powers")).show(truncate=False)
+---------------------------+
|powers |
+---------------------------+
|[1.0, 9.0, 625.0, 262144.0]|
+---------------------------+
>>> df = spark.createDataFrame([(1, ["foo", "bar"], [1, 2, 3])], ("id", "xs", "ys"))
>>> df.select(zip_with("xs", "ys", lambda x, y: concat_ws("_", x, y)).alias("xs_ys")).show()
+-----------------+
| xs_ys|
+-----------------+
|[foo_1, bar_2, 3]|
+-----------------+
"""
return _invoke_higher_order_function("zip_with", [left, right], [f])
@_try_remote_functions
def transform_keys(col: "ColumnOrName", f: Callable[[Column, Column], Column]) -> Column:
"""
Applies a function to every key-value pair in a map and returns
a map with the results of those applications as the new keys for the pairs.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
f : function
a binary function ``(k: Column, v: Column) -> Column...``
Can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
Returns
-------
:class:`~pyspark.sql.Column`
a new map of entries where new keys were calculated by applying given function to
each key value argument.
Examples
--------
>>> df = spark.createDataFrame([(1, {"foo": -2.0, "bar": 2.0})], ("id", "data"))
>>> row = df.select(transform_keys(
... "data", lambda k, _: upper(k)).alias("data_upper")
... ).head()
>>> sorted(row["data_upper"].items())
[('BAR', 2.0), ('FOO', -2.0)]
"""
return _invoke_higher_order_function("transform_keys", [col], [f])
@_try_remote_functions
def transform_values(col: "ColumnOrName", f: Callable[[Column, Column], Column]) -> Column:
"""
Applies a function to every key-value pair in a map and returns
a map with the results of those applications as the new values for the pairs.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
name of column or expression
f : function
a binary function ``(k: Column, v: Column) -> Column...``
Can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
Returns
-------
:class:`~pyspark.sql.Column`
a new map of entries where new values were calculated by applying given function to
each key value argument.
Examples
--------
>>> df = spark.createDataFrame([(1, {"IT": 10.0, "SALES": 2.0, "OPS": 24.0})], ("id", "data"))
>>> row = df.select(transform_values(
... "data", lambda k, v: when(k.isin("IT", "OPS"), v + 10.0).otherwise(v)
... ).alias("new_data")).head()
>>> sorted(row["new_data"].items())
[('IT', 20.0), ('OPS', 34.0), ('SALES', 2.0)]
"""
return _invoke_higher_order_function("transform_values", [col], [f])
@_try_remote_functions
def map_filter(col: "ColumnOrName", f: Callable[[Column, Column], Column]) -> Column:
"""
Collection function: Returns a new map column whose key-value pairs satisfy a given
predicate function.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
The name of the column or a column expression representing the map to be filtered.
f : function
A binary function ``(k: Column, v: Column) -> Column...`` that defines the predicate.
This function should return a boolean column that will be used to filter the input map.
Can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
Returns
-------
:class:`~pyspark.sql.Column`
A new map column containing only the key-value pairs that satisfy the predicate.
Examples
--------
Example 1: Filtering a map with a simple condition
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, {"foo": 42.0, "bar": 1.0, "baz": 32.0})], ("id", "data"))
>>> row = df.select(
... sf.map_filter("data", lambda _, v: v > 30.0).alias("data_filtered")
... ).head()
>>> sorted(row["data_filtered"].items())
[('baz', 32.0), ('foo', 42.0)]
Example 2: Filtering a map with a condition on keys
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, {"foo": 42.0, "bar": 1.0, "baz": 32.0})], ("id", "data"))
>>> row = df.select(
... sf.map_filter("data", lambda k, _: k.startswith("b")).alias("data_filtered")
... ).head()
>>> sorted(row["data_filtered"].items())
[('bar', 1.0), ('baz', 32.0)]
Example 3: Filtering a map with a complex condition
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, {"foo": 42.0, "bar": 1.0, "baz": 32.0})], ("id", "data"))
>>> row = df.select(
... sf.map_filter("data", lambda k, v: k.startswith("b") & (v > 1.0)).alias("data_filtered")
... ).head()
>>> sorted(row["data_filtered"].items())
[('baz', 32.0)]
"""
return _invoke_higher_order_function("map_filter", [col], [f])
@_try_remote_functions
def map_zip_with(
col1: "ColumnOrName",
col2: "ColumnOrName",
f: Callable[[Column, Column, Column], Column],
) -> Column:
"""
Collection: Merges two given maps into a single map by applying a function to
the key-value pairs.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or str
The name of the first column or a column expression representing the first map.
col2 : :class:`~pyspark.sql.Column` or str
The name of the second column or a column expression representing the second map.
f : function
A ternary function ``(k: Column, v1: Column, v2: Column) -> Column...`` that defines
how to merge the values from the two maps. This function should return a column that
will be used as the value in the resulting map.
Can use methods of :class:`~pyspark.sql.Column`, functions defined in
:py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
Python ``UserDefinedFunctions`` are not supported
(`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
Returns
-------
:class:`~pyspark.sql.Column`
A new map column where each key-value pair is the result of applying the function to
the corresponding key-value pairs in the input maps.
Examples
--------
Example 1: Merging two maps with a simple function
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (1, {"A": 1, "B": 2}, {"A": 3, "B": 4})],
... ("id", "map1", "map2"))
>>> row = df.select(
... sf.map_zip_with("map1", "map2", lambda _, v1, v2: v1 + v2).alias("updated_data")
... ).head()
>>> sorted(row["updated_data"].items())
[('A', 4), ('B', 6)]
Example 2: Merging two maps with a complex function
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (1, {"A": 1, "B": 2}, {"A": 3, "B": 4})],
... ("id", "map1", "map2"))
>>> row = df.select(
... sf.map_zip_with("map1", "map2",
... lambda k, v1, v2: sf.when(k == "A", v1 + v2).otherwise(v1 - v2)
... ).alias("updated_data")
... ).head()
>>> sorted(row["updated_data"].items())
[('A', 4), ('B', -2)]
Example 3: Merging two maps with mismatched keys
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([
... (1, {"A": 1, "B": 2}, {"B": 3, "C": 4})],
... ("id", "map1", "map2"))
>>> row = df.select(
... sf.map_zip_with("map1", "map2",
... lambda _, v1, v2: sf.when(v2.isNull(), v1).otherwise(v1 + v2)
... ).alias("updated_data")
... ).head()
>>> sorted(row["updated_data"].items())
[('A', 1), ('B', 5), ('C', None)]
"""
return _invoke_higher_order_function("map_zip_with", [col1, col2], [f])
@_try_remote_functions
def str_to_map(
text: "ColumnOrName",
pairDelim: Optional["ColumnOrName"] = None,
keyValueDelim: Optional["ColumnOrName"] = None,
) -> Column:
"""
Map function: Converts a string into a map after splitting the text into key/value pairs
using delimiters. Both `pairDelim` and `keyValueDelim` are treated as regular expressions.
.. versionadded:: 3.5.0
Parameters
----------
text : :class:`~pyspark.sql.Column` or str
Input column or strings.
pairDelim : :class:`~pyspark.sql.Column` or str, optional
Delimiter to use to split pairs. Default is comma (,).
keyValueDelim : :class:`~pyspark.sql.Column` or str, optional
Delimiter to use to split key/value. Default is colon (:).
Returns
-------
:class:`~pyspark.sql.Column`
A new column of map type where each string in the original column is converted into a map.
Examples
--------
Example 1: Using default delimiters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("a:1,b:2,c:3",)], ["e"])
>>> df.select(sf.str_to_map(df.e)).show(truncate=False)
+------------------------+
|str_to_map(e, ,, :) |
+------------------------+
|{a -> 1, b -> 2, c -> 3}|
+------------------------+
Example 2: Using custom delimiters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("a=1;b=2;c=3",)], ["e"])
>>> df.select(sf.str_to_map(df.e, sf.lit(";"), sf.lit("="))).show(truncate=False)
+------------------------+
|str_to_map(e, ;, =) |
+------------------------+
|{a -> 1, b -> 2, c -> 3}|
+------------------------+
Example 3: Using different delimiters for different rows
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("a:1,b:2,c:3",), ("d=4;e=5;f=6",)], ["e"])
>>> df.select(sf.str_to_map(df.e,
... sf.when(df.e.contains(";"), sf.lit(";")).otherwise(sf.lit(",")),
... sf.when(df.e.contains("="), sf.lit("=")).otherwise(sf.lit(":"))).alias("str_to_map")
... ).show(truncate=False)
+------------------------+
|str_to_map |
+------------------------+
|{a -> 1, b -> 2, c -> 3}|
|{d -> 4, e -> 5, f -> 6}|
+------------------------+
Example 4: Using a column of delimiters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("a:1,b:2,c:3", ","), ("d=4;e=5;f=6", ";")], ["e", "delim"])
>>> df.select(sf.str_to_map(df.e, df.delim, sf.lit(":"))).show(truncate=False)
+---------------------------------------+
|str_to_map(e, delim, :) |
+---------------------------------------+
|{a -> 1, b -> 2, c -> 3} |
|{d=4 -> NULL, e=5 -> NULL, f=6 -> NULL}|
+---------------------------------------+
Example 5: Using a column of key/value delimiters
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("a:1,b:2,c:3", ":"), ("d=4;e=5;f=6", "=")], ["e", "delim"])
>>> df.select(sf.str_to_map(df.e, sf.lit(","), df.delim)).show(truncate=False)
+------------------------+
|str_to_map(e, ,, delim) |
+------------------------+
|{a -> 1, b -> 2, c -> 3}|
|{d -> 4;e=5;f=6} |
+------------------------+
"""
if pairDelim is None:
pairDelim = lit(",")
if keyValueDelim is None:
keyValueDelim = lit(":")
return _invoke_function_over_columns("str_to_map", text, pairDelim, keyValueDelim)
# ---------------------- Partition transform functions --------------------------------
@_try_remote_functions
def years(col: "ColumnOrName") -> Column:
"""
Partition transform function: A transform for timestamps and dates
to partition data into years.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 4.0.0
Use :func:`partitioning.years` instead.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
target date or timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
data partitioned by years.
Examples
--------
>>> df.writeTo("catalog.db.table").partitionedBy( # doctest: +SKIP
... years("ts")
... ).createOrReplace()
Notes
-----
This function can be used only in combination with
:py:meth:`~pyspark.sql.readwriter.DataFrameWriterV2.partitionedBy`
method of the `DataFrameWriterV2`.
"""
from pyspark.sql.functions import partitioning
warnings.warn("Deprecated in 4.0.0, use partitioning.years instead.", FutureWarning)
return partitioning.years(col)
@_try_remote_functions
def months(col: "ColumnOrName") -> Column:
"""
Partition transform function: A transform for timestamps and dates
to partition data into months.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 4.0.0
Use :func:`partitioning.months` instead.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
target date or timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
data partitioned by months.
Examples
--------
>>> df.writeTo("catalog.db.table").partitionedBy(
... months("ts")
... ).createOrReplace() # doctest: +SKIP
Notes
-----
This function can be used only in combination with
:py:meth:`~pyspark.sql.readwriter.DataFrameWriterV2.partitionedBy`
method of the `DataFrameWriterV2`.
"""
from pyspark.sql.functions import partitioning
warnings.warn("Deprecated in 4.0.0, use partitioning.months instead.", FutureWarning)
return partitioning.months(col)
@_try_remote_functions
def days(col: "ColumnOrName") -> Column:
"""
Partition transform function: A transform for timestamps and dates
to partition data into days.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 4.0.0
Use :func:`partitioning.months` instead.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
target date or timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
data partitioned by days.
Examples
--------
>>> df.writeTo("catalog.db.table").partitionedBy( # doctest: +SKIP
... days("ts")
... ).createOrReplace()
Notes
-----
This function can be used only in combination with
:py:meth:`~pyspark.sql.readwriter.DataFrameWriterV2.partitionedBy`
method of the `DataFrameWriterV2`.
"""
from pyspark.sql.functions import partitioning
warnings.warn("Deprecated in 4.0.0, use partitioning.days instead.", FutureWarning)
return partitioning.days(col)
@_try_remote_functions
def hours(col: "ColumnOrName") -> Column:
"""
Partition transform function: A transform for timestamps
to partition data into hours.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 4.0.0
Use :func:`partitioning.hours` instead.
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
target date or timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
data partitioned by hours.
Examples
--------
>>> df.writeTo("catalog.db.table").partitionedBy( # doctest: +SKIP
... hours("ts")
... ).createOrReplace()
Notes
-----
This function can be used only in combination with
:py:meth:`~pyspark.sql.readwriter.DataFrameWriterV2.partitionedBy`
method of the `DataFrameWriterV2`.
"""
from pyspark.sql.functions import partitioning
warnings.warn("Deprecated in 4.0.0, use partitioning.hours instead.", FutureWarning)
return partitioning.hours(col)
@_try_remote_functions
def convert_timezone(
sourceTz: Optional[Column], targetTz: Column, sourceTs: "ColumnOrName"
) -> Column:
"""
Converts the timestamp without time zone `sourceTs`
from the `sourceTz` time zone to `targetTz`.
.. versionadded:: 3.5.0
Parameters
----------
sourceTz : :class:`~pyspark.sql.Column`, optional
The time zone for the input timestamp. If it is missed,
the current session time zone is used as the source time zone.
targetTz : :class:`~pyspark.sql.Column`
The time zone to which the input timestamp should be converted.
sourceTs : :class:`~pyspark.sql.Column` or column name
A timestamp without time zone.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a timestamp for converted time zone.
See Also
--------
:meth:`pyspark.sql.functions.current_timezone`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
Example 1: Converts the timestamp without time zone `sourceTs`.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08 00:00:00',)], ['ts'])
>>> df.select(
... '*',
... sf.convert_timezone(None, sf.lit('Asia/Hong_Kong'), 'ts')
... ).show() # doctest: +SKIP
+-------------------+--------------------------------------------------------+
| ts|convert_timezone(current_timezone(), Asia/Hong_Kong, ts)|
+-------------------+--------------------------------------------------------+
|2015-04-08 00:00:00| 2015-04-08 15:00:00|
+-------------------+--------------------------------------------------------+
Example 2: Converts the timestamp with time zone `sourceTs`.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('2015-04-08 15:00:00',)], ['ts'])
>>> df.select(
... '*',
... sf.convert_timezone(sf.lit('Asia/Hong_Kong'), sf.lit('America/Los_Angeles'), df.ts)
... ).show()
+-------------------+---------------------------------------------------------+
| ts|convert_timezone(Asia/Hong_Kong, America/Los_Angeles, ts)|
+-------------------+---------------------------------------------------------+
|2015-04-08 15:00:00| 2015-04-08 00:00:00|
+-------------------+---------------------------------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
if sourceTz is None:
return _invoke_function_over_columns("convert_timezone", targetTz, sourceTs)
else:
return _invoke_function_over_columns("convert_timezone", sourceTz, targetTz, sourceTs)
@_try_remote_functions
def make_dt_interval(
days: Optional["ColumnOrName"] = None,
hours: Optional["ColumnOrName"] = None,
mins: Optional["ColumnOrName"] = None,
secs: Optional["ColumnOrName"] = None,
) -> Column:
"""
Make DayTimeIntervalType duration from days, hours, mins and secs.
.. versionadded:: 3.5.0
Parameters
----------
days : :class:`~pyspark.sql.Column` or column name, optional
The number of days, positive or negative.
hours : :class:`~pyspark.sql.Column` or column name, optional
The number of hours, positive or negative.
mins : :class:`~pyspark.sql.Column` or column name, optional
The number of minutes, positive or negative.
secs : :class:`~pyspark.sql.Column` or column name, optional
The number of seconds with the fractional part in microsecond precision.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a DayTimeIntervalType duration.
See Also
--------
:meth:`pyspark.sql.functions.make_interval`
:meth:`pyspark.sql.functions.make_ym_interval`
:meth:`pyspark.sql.functions.try_make_interval`
Examples
--------
Example 1: Make DayTimeIntervalType duration from days, hours, mins and secs.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[1, 12, 30, 01.001001]], ['day', 'hour', 'min', 'sec'])
>>> df.select('*', sf.make_dt_interval(df.day, df.hour, df.min, df.sec)).show(truncate=False)
+---+----+---+--------+------------------------------------------+
|day|hour|min|sec |make_dt_interval(day, hour, min, sec) |
+---+----+---+--------+------------------------------------------+
|1 |12 |30 |1.001001|INTERVAL '1 12:30:01.001001' DAY TO SECOND|
+---+----+---+--------+------------------------------------------+
Example 2: Make DayTimeIntervalType duration from days, hours and mins.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[1, 12, 30, 01.001001]], ['day', 'hour', 'min', 'sec'])
>>> df.select('*', sf.make_dt_interval(df.day, 'hour', df.min)).show(truncate=False)
+---+----+---+--------+-----------------------------------+
|day|hour|min|sec |make_dt_interval(day, hour, min, 0)|
+---+----+---+--------+-----------------------------------+
|1 |12 |30 |1.001001|INTERVAL '1 12:30:00' DAY TO SECOND|
+---+----+---+--------+-----------------------------------+
Example 3: Make DayTimeIntervalType duration from days and hours.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[1, 12, 30, 01.001001]], ['day', 'hour', 'min', 'sec'])
>>> df.select('*', sf.make_dt_interval(df.day, df.hour)).show(truncate=False)
+---+----+---+--------+-----------------------------------+
|day|hour|min|sec |make_dt_interval(day, hour, 0, 0) |
+---+----+---+--------+-----------------------------------+
|1 |12 |30 |1.001001|INTERVAL '1 12:00:00' DAY TO SECOND|
+---+----+---+--------+-----------------------------------+
Example 4: Make DayTimeIntervalType duration from days.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[1, 12, 30, 01.001001]], ['day', 'hour', 'min', 'sec'])
>>> df.select('*', sf.make_dt_interval('day')).show(truncate=False)
+---+----+---+--------+-----------------------------------+
|day|hour|min|sec |make_dt_interval(day, 0, 0, 0) |
+---+----+---+--------+-----------------------------------+
|1 |12 |30 |1.001001|INTERVAL '1 00:00:00' DAY TO SECOND|
+---+----+---+--------+-----------------------------------+
Example 5: Make empty interval.
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.make_dt_interval()).show(truncate=False)
+-----------------------------------+
|make_dt_interval(0, 0, 0, 0) |
+-----------------------------------+
|INTERVAL '0 00:00:00' DAY TO SECOND|
+-----------------------------------+
"""
_days = lit(0) if days is None else days
_hours = lit(0) if hours is None else hours
_mins = lit(0) if mins is None else mins
_secs = lit(decimal.Decimal(0)) if secs is None else secs
return _invoke_function_over_columns("make_dt_interval", _days, _hours, _mins, _secs)
@_try_remote_functions
def try_make_interval(
years: Optional["ColumnOrName"] = None,
months: Optional["ColumnOrName"] = None,
weeks: Optional["ColumnOrName"] = None,
days: Optional["ColumnOrName"] = None,
hours: Optional["ColumnOrName"] = None,
mins: Optional["ColumnOrName"] = None,
secs: Optional["ColumnOrName"] = None,
) -> Column:
"""
This is a special version of `make_interval` that performs the same operation, but returns a
NULL value instead of raising an error if interval cannot be created.
.. versionadded:: 4.0.0
Parameters
----------
years : :class:`~pyspark.sql.Column` or column name, optional
The number of years, positive or negative.
months : :class:`~pyspark.sql.Column` or column name, optional
The number of months, positive or negative.
weeks : :class:`~pyspark.sql.Column` or column name, optional
The number of weeks, positive or negative.
days : :class:`~pyspark.sql.Column` or column name, optional
The number of days, positive or negative.
hours : :class:`~pyspark.sql.Column` or column name, optional
The number of hours, positive or negative.
mins : :class:`~pyspark.sql.Column` or column name, optional
The number of minutes, positive or negative.
secs : :class:`~pyspark.sql.Column` or column name, optional
The number of seconds with the fractional part in microsecond precision.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains an interval.
See Also
--------
:meth:`pyspark.sql.functions.make_interval`
:meth:`pyspark.sql.functions.make_dt_interval`
:meth:`pyspark.sql.functions.make_ym_interval`
Examples
--------
Example 1: Try make interval from years, months, weeks, days, hours, mins and secs.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(
... sf.try_make_interval(df.year, df.month, 'week', df.day, 'hour', df.min, df.sec)
... ).show(truncate=False)
+---------------------------------------------------------------+
|try_make_interval(year, month, week, day, hour, min, sec) |
+---------------------------------------------------------------+
|100 years 11 months 8 days 12 hours 30 minutes 1.001001 seconds|
+---------------------------------------------------------------+
Example 2: Try make interval from years, months, weeks, days, hours and mins.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(
... sf.try_make_interval(df.year, df.month, 'week', df.day, df.hour, df.min)
... ).show(truncate=False)
+-------------------------------------------------------+
|try_make_interval(year, month, week, day, hour, min, 0)|
+-------------------------------------------------------+
|100 years 11 months 8 days 12 hours 30 minutes |
+-------------------------------------------------------+
Example 3: Try make interval from years, months, weeks, days and hours.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(
... sf.try_make_interval(df.year, df.month, 'week', df.day, df.hour)
... ).show(truncate=False)
+-----------------------------------------------------+
|try_make_interval(year, month, week, day, hour, 0, 0)|
+-----------------------------------------------------+
|100 years 11 months 8 days 12 hours |
+-----------------------------------------------------+
Example 4: Try make interval from years, months, weeks and days.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(sf.try_make_interval(df.year, 'month', df.week, df.day)).show(truncate=False)
+--------------------------------------------------+
|try_make_interval(year, month, week, day, 0, 0, 0)|
+--------------------------------------------------+
|100 years 11 months 8 days |
+--------------------------------------------------+
Example 5: Try make interval from years, months and weeks.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(sf.try_make_interval(df.year, 'month', df.week)).show(truncate=False)
+------------------------------------------------+
|try_make_interval(year, month, week, 0, 0, 0, 0)|
+------------------------------------------------+
|100 years 11 months 7 days |
+------------------------------------------------+
Example 6: Try make interval from years and months.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(sf.try_make_interval(df.year, 'month')).show(truncate=False)
+---------------------------------------------+
|try_make_interval(year, month, 0, 0, 0, 0, 0)|
+---------------------------------------------+
|100 years 11 months |
+---------------------------------------------+
Example 7: Try make interval from years.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(sf.try_make_interval(df.year)).show(truncate=False)
+-----------------------------------------+
|try_make_interval(year, 0, 0, 0, 0, 0, 0)|
+-----------------------------------------+
|100 years |
+-----------------------------------------+
Example 8: Try make empty interval.
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.try_make_interval()).show(truncate=False)
+--------------------------------------+
|try_make_interval(0, 0, 0, 0, 0, 0, 0)|
+--------------------------------------+
|0 seconds |
+--------------------------------------+
Example 9: Try make interval from years with overflow.
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.try_make_interval(sf.lit(2147483647))).show(truncate=False)
+-----------------------------------------------+
|try_make_interval(2147483647, 0, 0, 0, 0, 0, 0)|
+-----------------------------------------------+
|NULL |
+-----------------------------------------------+
"""
_years = lit(0) if years is None else years
_months = lit(0) if months is None else months
_weeks = lit(0) if weeks is None else weeks
_days = lit(0) if days is None else days
_hours = lit(0) if hours is None else hours
_mins = lit(0) if mins is None else mins
_secs = lit(decimal.Decimal(0)) if secs is None else secs
return _invoke_function_over_columns(
"try_make_interval", _years, _months, _weeks, _days, _hours, _mins, _secs
)
@_try_remote_functions
def make_interval(
years: Optional["ColumnOrName"] = None,
months: Optional["ColumnOrName"] = None,
weeks: Optional["ColumnOrName"] = None,
days: Optional["ColumnOrName"] = None,
hours: Optional["ColumnOrName"] = None,
mins: Optional["ColumnOrName"] = None,
secs: Optional["ColumnOrName"] = None,
) -> Column:
"""
Make interval from years, months, weeks, days, hours, mins and secs.
.. versionadded:: 3.5.0
Parameters
----------
years : :class:`~pyspark.sql.Column` or column name, optional
The number of years, positive or negative.
months : :class:`~pyspark.sql.Column` or column name, optional
The number of months, positive or negative.
weeks : :class:`~pyspark.sql.Column` or column name, optional
The number of weeks, positive or negative.
days : :class:`~pyspark.sql.Column` or column name, optional
The number of days, positive or negative.
hours : :class:`~pyspark.sql.Column` or column name, optional
The number of hours, positive or negative.
mins : :class:`~pyspark.sql.Column` or column name, optional
The number of minutes, positive or negative.
secs : :class:`~pyspark.sql.Column` or column name, optional
The number of seconds with the fractional part in microsecond precision.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains an interval.
See Also
--------
:meth:`pyspark.sql.functions.make_dt_interval`
:meth:`pyspark.sql.functions.make_ym_interval`
:meth:`pyspark.sql.functions.try_make_interval`
Examples
--------
Example 1: Make interval from years, months, weeks, days, hours, mins and secs.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(
... sf.make_interval(df.year, df.month, 'week', df.day, df.hour, df.min, df.sec)
... ).show(truncate=False)
+---------------------------------------------------------------+
|make_interval(year, month, week, day, hour, min, sec) |
+---------------------------------------------------------------+
|100 years 11 months 8 days 12 hours 30 minutes 1.001001 seconds|
+---------------------------------------------------------------+
Example 2: Make interval from years, months, weeks, days, hours and mins.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(
... sf.make_interval(df.year, df.month, 'week', df.day, df.hour, df.min)
... ).show(truncate=False)
+---------------------------------------------------+
|make_interval(year, month, week, day, hour, min, 0)|
+---------------------------------------------------+
|100 years 11 months 8 days 12 hours 30 minutes |
+---------------------------------------------------+
Example 3: Make interval from years, months, weeks, days and hours.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(
... sf.make_interval(df.year, df.month, 'week', df.day, df.hour)
... ).show(truncate=False)
+-------------------------------------------------+
|make_interval(year, month, week, day, hour, 0, 0)|
+-------------------------------------------------+
|100 years 11 months 8 days 12 hours |
+-------------------------------------------------+
Example 4: Make interval from years, months, weeks and days.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(sf.make_interval(df.year, df.month, 'week', df.day)).show(truncate=False)
+----------------------------------------------+
|make_interval(year, month, week, day, 0, 0, 0)|
+----------------------------------------------+
|100 years 11 months 8 days |
+----------------------------------------------+
Example 5: Make interval from years, months and weeks.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(sf.make_interval(df.year, df.month, 'week')).show(truncate=False)
+--------------------------------------------+
|make_interval(year, month, week, 0, 0, 0, 0)|
+--------------------------------------------+
|100 years 11 months 7 days |
+--------------------------------------------+
Example 6: Make interval from years and months.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(sf.make_interval(df.year, df.month)).show(truncate=False)
+-----------------------------------------+
|make_interval(year, month, 0, 0, 0, 0, 0)|
+-----------------------------------------+
|100 years 11 months |
+-----------------------------------------+
Example 7: Make interval from years.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[100, 11, 1, 1, 12, 30, 01.001001]],
... ['year', 'month', 'week', 'day', 'hour', 'min', 'sec'])
>>> df.select(sf.make_interval(df.year)).show(truncate=False)
+-------------------------------------+
|make_interval(year, 0, 0, 0, 0, 0, 0)|
+-------------------------------------+
|100 years |
+-------------------------------------+
Example 8: Make empty interval.
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.make_interval()).show(truncate=False)
+----------------------------------+
|make_interval(0, 0, 0, 0, 0, 0, 0)|
+----------------------------------+
|0 seconds |
+----------------------------------+
"""
_years = lit(0) if years is None else years
_months = lit(0) if months is None else months
_weeks = lit(0) if weeks is None else weeks
_days = lit(0) if days is None else days
_hours = lit(0) if hours is None else hours
_mins = lit(0) if mins is None else mins
_secs = lit(decimal.Decimal(0)) if secs is None else secs
return _invoke_function_over_columns(
"make_interval", _years, _months, _weeks, _days, _hours, _mins, _secs
)
@_try_remote_functions
def make_time(hour: "ColumnOrName", minute: "ColumnOrName", second: "ColumnOrName") -> Column:
"""
Create time from hour, minute and second fields. For invalid inputs it will throw an error.
.. versionadded:: 4.1.0
Parameters
----------
hour : :class:`~pyspark.sql.Column` or column name
The hour to represent, from 0 to 23.
minute : :class:`~pyspark.sql.Column` or column name
The minute to represent, from 0 to 59.
second : :class:`~pyspark.sql.Column` or column name
The second to represent, from 0 to 59.999999.
Returns
-------
:class:`~pyspark.sql.Column`
A column representing the created time.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(6, 30, 45.887)], ["hour", "minute", "second"])
>>> df.select(sf.make_time("hour", "minute", "second").alias("time")).show()
+------------+
| time|
+------------+
|06:30:45.887|
+------------+
"""
return _invoke_function_over_columns("make_time", hour, minute, second)
@_try_remote_functions
def make_timestamp(
years: "ColumnOrName",
months: "ColumnOrName",
days: "ColumnOrName",
hours: "ColumnOrName",
mins: "ColumnOrName",
secs: "ColumnOrName",
timezone: Optional["ColumnOrName"] = None,
) -> Column:
"""
Create timestamp from years, months, days, hours, mins, secs and timezone fields.
The result data type is consistent with the value of configuration `spark.sql.timestampType`.
If the configuration `spark.sql.ansi.enabled` is false, the function returns NULL
on invalid inputs. Otherwise, it will throw an error instead.
.. versionadded:: 3.5.0
Parameters
----------
years : :class:`~pyspark.sql.Column` or column name
The year to represent, from 1 to 9999
months : :class:`~pyspark.sql.Column` or column name
The month-of-year to represent, from 1 (January) to 12 (December)
days : :class:`~pyspark.sql.Column` or column name
The day-of-month to represent, from 1 to 31
hours : :class:`~pyspark.sql.Column` or column name
The hour-of-day to represent, from 0 to 23
mins : :class:`~pyspark.sql.Column` or column name
The minute-of-hour to represent, from 0 to 59
secs : :class:`~pyspark.sql.Column` or column name
The second-of-minute and its micro-fraction to represent, from 0 to 60.
The value can be either an integer like 13 , or a fraction like 13.123.
If the sec argument equals to 60, the seconds field is set
to 0 and 1 minute is added to the final timestamp.
timezone : :class:`~pyspark.sql.Column` or column name, optional
The time zone identifier. For example, CET, UTC and etc.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a timestamp.
See Also
--------
:meth:`pyspark.sql.functions.make_timestamp_ltz`
:meth:`pyspark.sql.functions.make_timestamp_ntz`
:meth:`pyspark.sql.functions.try_make_timestamp`
:meth:`pyspark.sql.functions.try_make_timestamp_ltz`
:meth:`pyspark.sql.functions.try_make_timestamp_ntz`
:meth:`pyspark.sql.functions.make_time`
:meth:`pyspark.sql.functions.make_interval`
:meth:`pyspark.sql.functions.try_make_interval`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
Example 1: Make timestamp from years, months, days, hours, mins and secs.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12, 28, 6, 30, 45.887, 'CET']],
... ['year', 'month', 'day', 'hour', 'min', 'sec', 'tz'])
>>> df.select(
... sf.make_timestamp(df.year, df.month, df.day, 'hour', df.min, df.sec, 'tz')
... ).show(truncate=False)
+----------------------------------------------------+
|make_timestamp(year, month, day, hour, min, sec, tz)|
+----------------------------------------------------+
|2014-12-27 21:30:45.887 |
+----------------------------------------------------+
Example 2: Make timestamp without timezone.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12, 28, 6, 30, 45.887, 'CET']],
... ['year', 'month', 'day', 'hour', 'min', 'sec', 'tz'])
>>> df.select(
... sf.make_timestamp(df.year, df.month, df.day, 'hour', df.min, df.sec)
... ).show(truncate=False)
+------------------------------------------------+
|make_timestamp(year, month, day, hour, min, sec)|
+------------------------------------------------+
|2014-12-28 06:30:45.887 |
+------------------------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
if timezone is not None:
return _invoke_function_over_columns(
"make_timestamp", years, months, days, hours, mins, secs, timezone
)
else:
return _invoke_function_over_columns(
"make_timestamp", years, months, days, hours, mins, secs
)
@_try_remote_functions
def try_make_timestamp(
years: "ColumnOrName",
months: "ColumnOrName",
days: "ColumnOrName",
hours: "ColumnOrName",
mins: "ColumnOrName",
secs: "ColumnOrName",
timezone: Optional["ColumnOrName"] = None,
) -> Column:
"""
Try to create timestamp from years, months, days, hours, mins, secs and timezone fields.
The result data type is consistent with the value of configuration `spark.sql.timestampType`.
The function returns NULL on invalid inputs.
.. versionadded:: 4.0.0
Parameters
----------
years : :class:`~pyspark.sql.Column` or column name
The year to represent, from 1 to 9999
months : :class:`~pyspark.sql.Column` or column name
The month-of-year to represent, from 1 (January) to 12 (December)
days : :class:`~pyspark.sql.Column` or column name
The day-of-month to represent, from 1 to 31
hours : :class:`~pyspark.sql.Column` or column name
The hour-of-day to represent, from 0 to 23
mins : :class:`~pyspark.sql.Column` or column name
The minute-of-hour to represent, from 0 to 59
secs : :class:`~pyspark.sql.Column` or column name
The second-of-minute and its micro-fraction to represent, from 0 to 60.
The value can be either an integer like 13 , or a fraction like 13.123.
If the sec argument equals to 60, the seconds field is set
to 0 and 1 minute is added to the final timestamp.
timezone : :class:`~pyspark.sql.Column` or column name, optional
The time zone identifier. For example, CET, UTC and etc.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a timestamp or NULL in case of an error.
See Also
--------
:meth:`pyspark.sql.functions.make_timestamp`
:meth:`pyspark.sql.functions.make_timestamp_ltz`
:meth:`pyspark.sql.functions.make_timestamp_ntz`
:meth:`pyspark.sql.functions.try_make_timestamp_ltz`
:meth:`pyspark.sql.functions.try_make_timestamp_ntz`
:meth:`pyspark.sql.functions.make_time`
:meth:`pyspark.sql.functions.make_interval`
:meth:`pyspark.sql.functions.try_make_interval`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
Example 1: Make timestamp from years, months, days, hours, mins and secs.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12, 28, 6, 30, 45.887, 'CET']],
... ['year', 'month', 'day', 'hour', 'min', 'sec', 'tz'])
>>> df.select(
... sf.try_make_timestamp(df.year, df.month, df.day, 'hour', df.min, df.sec, 'tz')
... ).show(truncate=False)
+----------------------------------------------------+
|try_make_timestamp(year, month, day, hour, min, sec)|
+----------------------------------------------------+
|2014-12-27 21:30:45.887 |
+----------------------------------------------------+
Example 2: Make timestamp without timezone.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12, 28, 6, 30, 45.887, 'CET']],
... ['year', 'month', 'day', 'hour', 'min', 'sec', 'tz'])
>>> df.select(
... sf.try_make_timestamp(df.year, df.month, df.day, 'hour', df.min, df.sec)
... ).show(truncate=False)
+----------------------------------------------------+
|try_make_timestamp(year, month, day, hour, min, sec)|
+----------------------------------------------------+
|2014-12-28 06:30:45.887 |
+----------------------------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
Example 3: Make timestamp with invalid input.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 13, 28, 6, 30, 45.887, 'CET']],
... ['year', 'month', 'day', 'hour', 'min', 'sec', 'tz'])
>>> df.select(
... sf.try_make_timestamp(df.year, df.month, df.day, 'hour', df.min, df.sec)
... ).show(truncate=False)
+----------------------------------------------------+
|try_make_timestamp(year, month, day, hour, min, sec)|
+----------------------------------------------------+
|NULL |
+----------------------------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
if timezone is not None:
return _invoke_function_over_columns(
"try_make_timestamp", years, months, days, hours, mins, secs, timezone
)
else:
return _invoke_function_over_columns(
"try_make_timestamp", years, months, days, hours, mins, secs
)
@_try_remote_functions
def make_timestamp_ltz(
years: "ColumnOrName",
months: "ColumnOrName",
days: "ColumnOrName",
hours: "ColumnOrName",
mins: "ColumnOrName",
secs: "ColumnOrName",
timezone: Optional["ColumnOrName"] = None,
) -> Column:
"""
Create the current timestamp with local time zone from years, months, days, hours, mins,
secs and timezone fields. If the configuration `spark.sql.ansi.enabled` is false,
the function returns NULL on invalid inputs. Otherwise, it will throw an error instead.
.. versionadded:: 3.5.0
Parameters
----------
years : :class:`~pyspark.sql.Column` or str
The year to represent, from 1 to 9999
months : :class:`~pyspark.sql.Column` or str
The month-of-year to represent, from 1 (January) to 12 (December)
days : :class:`~pyspark.sql.Column` or str
The day-of-month to represent, from 1 to 31
hours : :class:`~pyspark.sql.Column` or str
The hour-of-day to represent, from 0 to 23
mins : :class:`~pyspark.sql.Column` or str
The minute-of-hour to represent, from 0 to 59
secs : :class:`~pyspark.sql.Column` or str
The second-of-minute and its micro-fraction to represent, from 0 to 60.
The value can be either an integer like 13 , or a fraction like 13.123.
If the sec argument equals to 60, the seconds field is set
to 0 and 1 minute is added to the final timestamp.
timezone : :class:`~pyspark.sql.Column` or str, optional
The time zone identifier. For example, CET, UTC and etc.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a current timestamp.
See Also
--------
:meth:`pyspark.sql.functions.make_timestamp`
:meth:`pyspark.sql.functions.make_timestamp_ntz`
:meth:`pyspark.sql.functions.try_make_timestamp`
:meth:`pyspark.sql.functions.try_make_timestamp_ltz`
:meth:`pyspark.sql.functions.try_make_timestamp_ntz`
:meth:`pyspark.sql.functions.make_time`
:meth:`pyspark.sql.functions.make_interval`
:meth:`pyspark.sql.functions.try_make_interval`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
Example 1: Make the current timestamp from years, months, days, hours, mins and secs.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12, 28, 6, 30, 45.887, 'CET']],
... ['year', 'month', 'day', 'hour', 'min', 'sec', 'tz'])
>>> df.select(
... sf.make_timestamp_ltz(df.year, df.month, 'day', df.hour, df.min, df.sec, 'tz')
... ).show(truncate=False)
+--------------------------------------------------------+
|make_timestamp_ltz(year, month, day, hour, min, sec, tz)|
+--------------------------------------------------------+
|2014-12-27 21:30:45.887 |
+--------------------------------------------------------+
Example 2: Make the current timestamp without timezone.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12, 28, 6, 30, 45.887, 'CET']],
... ['year', 'month', 'day', 'hour', 'min', 'sec', 'tz'])
>>> df.select(
... sf.make_timestamp_ltz(df.year, df.month, 'day', df.hour, df.min, df.sec)
... ).show(truncate=False)
+----------------------------------------------------+
|make_timestamp_ltz(year, month, day, hour, min, sec)|
+----------------------------------------------------+
|2014-12-28 06:30:45.887 |
+----------------------------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
if timezone is not None:
return _invoke_function_over_columns(
"make_timestamp_ltz", years, months, days, hours, mins, secs, timezone
)
else:
return _invoke_function_over_columns(
"make_timestamp_ltz", years, months, days, hours, mins, secs
)
@_try_remote_functions
def try_make_timestamp_ltz(
years: "ColumnOrName",
months: "ColumnOrName",
days: "ColumnOrName",
hours: "ColumnOrName",
mins: "ColumnOrName",
secs: "ColumnOrName",
timezone: Optional["ColumnOrName"] = None,
) -> Column:
"""
Try to create the current timestamp with local time zone from years, months, days, hours, mins,
secs and timezone fields.
The function returns NULL on invalid inputs.
.. versionadded:: 4.0.0
Parameters
----------
years : :class:`~pyspark.sql.Column` or column name
The year to represent, from 1 to 9999
months : :class:`~pyspark.sql.Column` or column name
The month-of-year to represent, from 1 (January) to 12 (December)
days : :class:`~pyspark.sql.Column` or column name
The day-of-month to represent, from 1 to 31
hours : :class:`~pyspark.sql.Column` or column name
The hour-of-day to represent, from 0 to 23
mins : :class:`~pyspark.sql.Column` or column name
The minute-of-hour to represent, from 0 to 59
secs : :class:`~pyspark.sql.Column` or column name
The second-of-minute and its micro-fraction to represent, from 0 to 60.
The value can be either an integer like 13 , or a fraction like 13.123.
If the sec argument equals to 60, the seconds field is set
to 0 and 1 minute is added to the final timestamp.
timezone : :class:`~pyspark.sql.Column` or column name, optional
The time zone identifier. For example, CET, UTC and etc.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a current timestamp, or NULL in case of an error.
See Also
--------
:meth:`pyspark.sql.functions.make_timestamp`
:meth:`pyspark.sql.functions.make_timestamp_ltz`
:meth:`pyspark.sql.functions.make_timestamp_ntz`
:meth:`pyspark.sql.functions.try_make_timestamp`
:meth:`pyspark.sql.functions.try_make_timestamp_ntz`
:meth:`pyspark.sql.functions.make_time`
:meth:`pyspark.sql.functions.make_interval`
:meth:`pyspark.sql.functions.try_make_interval`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
Example 1: Make the current timestamp from years, months, days, hours, mins and secs.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12, 28, 6, 30, 45.887, 'CET']],
... ['year', 'month', 'day', 'hour', 'min', 'sec', 'tz'])
>>> df.select(
... sf.try_make_timestamp_ltz('year', 'month', df.day, df.hour, df.min, df.sec, 'tz')
... ).show(truncate=False)
+------------------------------------------------------------+
|try_make_timestamp_ltz(year, month, day, hour, min, sec, tz)|
+------------------------------------------------------------+
|2014-12-27 21:30:45.887 |
+------------------------------------------------------------+
Example 2: Make the current timestamp without timezone.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12, 28, 6, 30, 45.887, 'CET']],
... ['year', 'month', 'day', 'hour', 'min', 'sec', 'tz'])
>>> df.select(
... sf.try_make_timestamp_ltz('year', 'month', df.day, df.hour, df.min, df.sec)
... ).show(truncate=False)
+--------------------------------------------------------+
|try_make_timestamp_ltz(year, month, day, hour, min, sec)|
+--------------------------------------------------------+
|2014-12-28 06:30:45.887 |
+--------------------------------------------------------+
Example 3: Make the current timestamp with invalid input.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 13, 28, 6, 30, 45.887, 'CET']],
... ['year', 'month', 'day', 'hour', 'min', 'sec', 'tz'])
>>> df.select(
... sf.try_make_timestamp_ltz('year', 'month', df.day, df.hour, df.min, df.sec)
... ).show(truncate=False)
+--------------------------------------------------------+
|try_make_timestamp_ltz(year, month, day, hour, min, sec)|
+--------------------------------------------------------+
|NULL |
+--------------------------------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
if timezone is not None:
return _invoke_function_over_columns(
"try_make_timestamp_ltz", years, months, days, hours, mins, secs, timezone
)
else:
return _invoke_function_over_columns(
"try_make_timestamp_ltz", years, months, days, hours, mins, secs
)
@overload
def make_timestamp_ntz(
years: "ColumnOrName",
months: "ColumnOrName",
days: "ColumnOrName",
hours: "ColumnOrName",
mins: "ColumnOrName",
secs: "ColumnOrName",
) -> Column:
...
@overload
def make_timestamp_ntz(
*,
date: "ColumnOrName",
time: "ColumnOrName",
) -> Column:
...
@_try_remote_functions
def make_timestamp_ntz(
years: Optional["ColumnOrName"] = None,
months: Optional["ColumnOrName"] = None,
days: Optional["ColumnOrName"] = None,
hours: Optional["ColumnOrName"] = None,
mins: Optional["ColumnOrName"] = None,
secs: Optional["ColumnOrName"] = None,
date: Optional["ColumnOrName"] = None,
time: Optional["ColumnOrName"] = None,
) -> Column:
"""
Create local date-time from years, months, days, hours, mins, secs fields. Alternatively, try to
create local date-time from date and time fields. If the configuration `spark.sql.ansi.enabled`
is false, the function returns NULL on invalid inputs. Otherwise, it will throw an error.
.. versionadded:: 3.5.0
.. versionchanged:: 4.1.0
Added support for creating timestamps from date and time.
Parameters
----------
years : :class:`~pyspark.sql.Column` or column name, optional
The year to represent, from 1 to 9999.
Required when creating timestamps from individual components.
Must be used with months, days, hours, mins, and secs.
months : :class:`~pyspark.sql.Column` or column name, optional
The month-of-year to represent, from 1 (January) to 12 (December).
Required when creating timestamps from individual components.
Must be used with years, days, hours, mins, and secs.
days : :class:`~pyspark.sql.Column` or column name, optional
The day-of-month to represent, from 1 to 31.
Required when creating timestamps from individual components.
Must be used with years, months, hours, mins, and secs.
hours : :class:`~pyspark.sql.Column` or column name, optional
The hour-of-day to represent, from 0 to 23.
Required when creating timestamps from individual components.
Must be used with years, months, days, mins, and secs.
mins : :class:`~pyspark.sql.Column` or column name, optional
The minute-of-hour to represent, from 0 to 59.
Required when creating timestamps from individual components.
Must be used with years, months, days, hours, and secs.
secs : :class:`~pyspark.sql.Column` or column name, optional
The second-of-minute and its micro-fraction to represent, from 0 to 60.
The value can be either an integer like 13, or a fraction like 13.123.
If the sec argument equals to 60, the seconds field is set
to 0 and 1 minute is added to the final timestamp.
Required when creating timestamps from individual components.
Must be used with years, months, days, hours, and mins.
date : :class:`~pyspark.sql.Column` or column name, optional
The date to represent, in valid DATE format.
Required when creating timestamps from date and time components.
Must be used with time parameter only.
time : :class:`~pyspark.sql.Column` or column name, optional
The time to represent, in valid TIME format.
Required when creating timestamps from date and time components.
Must be used with date parameter only.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a local date-time.
See Also
--------
:meth:`pyspark.sql.functions.make_timestamp`
:meth:`pyspark.sql.functions.make_timestamp_ltz`
:meth:`pyspark.sql.functions.try_make_timestamp`
:meth:`pyspark.sql.functions.try_make_timestamp_ltz`
:meth:`pyspark.sql.functions.try_make_timestamp_ntz`
:meth:`pyspark.sql.functions.make_time`
:meth:`pyspark.sql.functions.make_interval`
:meth:`pyspark.sql.functions.try_make_interval`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
Example 1: Make local date-time from years, months, days, hours, mins, secs.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12, 28, 6, 30, 45.887]],
... ['year', 'month', 'day', 'hour', 'min', 'sec'])
>>> df.select(
... sf.make_timestamp_ntz('year', 'month', df.day, df.hour, df.min, df.sec)
... ).show(truncate=False)
+----------------------------------------------------+
|make_timestamp_ntz(year, month, day, hour, min, sec)|
+----------------------------------------------------+
|2014-12-28 06:30:45.887 |
+----------------------------------------------------+
Example 2: Make local date-time from date and time.
>>> import pyspark.sql.functions as sf
>>> from datetime import date, time
>>> df = spark.range(1).select(
... sf.lit(date(2014, 12, 28)).alias("date"),
... sf.lit(time(6, 30, 45, 887000)).alias("time")
... )
>>> df.select(sf.make_timestamp_ntz(date=df.date, time=df.time)).show(truncate=False)
+------------------------------+
|make_timestamp_ntz(date, time)|
+------------------------------+
|2014-12-28 06:30:45.887 |
+------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
if years is not None:
if any(arg is not None for arg in [date, time]):
raise PySparkValueError(
errorClass="CANNOT_SET_TOGETHER",
messageParameters={"arg_list": "years|months|days|hours|mins|secs and date|time"},
)
return _invoke_function_over_columns(
"make_timestamp_ntz",
cast("ColumnOrName", years),
cast("ColumnOrName", months),
cast("ColumnOrName", days),
cast("ColumnOrName", hours),
cast("ColumnOrName", mins),
cast("ColumnOrName", secs),
)
else:
if any(arg is not None for arg in [years, months, days, hours, mins, secs]):
raise PySparkValueError(
errorClass="CANNOT_SET_TOGETHER",
messageParameters={"arg_list": "years|months|days|hours|mins|secs and date|time"},
)
return _invoke_function_over_columns(
"make_timestamp_ntz", cast("ColumnOrName", date), cast("ColumnOrName", time)
)
@overload
def try_make_timestamp_ntz(
years: "ColumnOrName",
months: "ColumnOrName",
days: "ColumnOrName",
hours: "ColumnOrName",
mins: "ColumnOrName",
secs: "ColumnOrName",
) -> Column:
...
@overload
def try_make_timestamp_ntz(
*,
date: "ColumnOrName",
time: "ColumnOrName",
) -> Column:
...
@_try_remote_functions
def try_make_timestamp_ntz(
years: Optional["ColumnOrName"] = None,
months: Optional["ColumnOrName"] = None,
days: Optional["ColumnOrName"] = None,
hours: Optional["ColumnOrName"] = None,
mins: Optional["ColumnOrName"] = None,
secs: Optional["ColumnOrName"] = None,
date: Optional["ColumnOrName"] = None,
time: Optional["ColumnOrName"] = None,
) -> Column:
"""
Try to create local date-time from years, months, days, hours, mins, secs fields. Alternatively,
try to create local date-time from date and time fields. The function returns NULL on invalid
inputs.
.. versionadded:: 4.0.0
.. versionchanged:: 4.1.0
Added support for creating timestamps from date and time.
Parameters
----------
years : :class:`~pyspark.sql.Column` or column name, optional
The year to represent, from 1 to 9999.
Required when creating timestamps from individual components.
Must be used with months, days, hours, mins, and secs.
months : :class:`~pyspark.sql.Column` or column name, optional
The month-of-year to represent, from 1 (January) to 12 (December).
Required when creating timestamps from individual components.
Must be used with years, days, hours, mins, and secs.
days : :class:`~pyspark.sql.Column` or column name, optional
The day-of-month to represent, from 1 to 31.
Required when creating timestamps from individual components.
Must be used with years, months, hours, mins, and secs.
hours : :class:`~pyspark.sql.Column` or column name, optional
The hour-of-day to represent, from 0 to 23.
Required when creating timestamps from individual components.
Must be used with years, months, days, mins, and secs.
mins : :class:`~pyspark.sql.Column` or column name, optional
The minute-of-hour to represent, from 0 to 59.
Required when creating timestamps from individual components.
Must be used with years, months, days, hours, and secs.
secs : :class:`~pyspark.sql.Column` or column name, optional
The second-of-minute and its micro-fraction to represent, from 0 to 60.
The value can be either an integer like 13, or a fraction like 13.123.
If the sec argument equals to 60, the seconds field is set
to 0 and 1 minute is added to the final timestamp.
Required when creating timestamps from individual components.
Must be used with years, months, days, hours, and mins.
date : :class:`~pyspark.sql.Column` or column name, optional
The date to represent, in valid DATE format.
Required when creating timestamps from date and time components.
Must be used with time parameter only.
time : :class:`~pyspark.sql.Column` or column name, optional
The time to represent, in valid TIME format.
Required when creating timestamps from date and time components.
Must be used with date parameter only.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a local date-time, or NULL in case of an error.
See Also
--------
:meth:`pyspark.sql.functions.make_timestamp`
:meth:`pyspark.sql.functions.make_timestamp_ltz`
:meth:`pyspark.sql.functions.make_timestamp_ntz`
:meth:`pyspark.sql.functions.try_make_timestamp`
:meth:`pyspark.sql.functions.try_make_timestamp_ltz`
:meth:`pyspark.sql.functions.make_time`
:meth:`pyspark.sql.functions.make_interval`
:meth:`pyspark.sql.functions.try_make_interval`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
Example 1: Make local date-time from years, months, days, hours, mins, secs.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12, 28, 6, 30, 45.887]],
... ['year', 'month', 'day', 'hour', 'min', 'sec'])
>>> df.select(
... sf.try_make_timestamp_ntz('year', 'month', df.day, df.hour, df.min, df.sec)
... ).show(truncate=False)
+--------------------------------------------------------+
|try_make_timestamp_ntz(year, month, day, hour, min, sec)|
+--------------------------------------------------------+
|2014-12-28 06:30:45.887 |
+--------------------------------------------------------+
Example 2: Make local date-time with invalid input
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 13, 28, 6, 30, 45.887]],
... ['year', 'month', 'day', 'hour', 'min', 'sec'])
>>> df.select(
... sf.try_make_timestamp_ntz('year', 'month', df.day, df.hour, df.min, df.sec)
... ).show(truncate=False)
+--------------------------------------------------------+
|try_make_timestamp_ntz(year, month, day, hour, min, sec)|
+--------------------------------------------------------+
|NULL |
+--------------------------------------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
if years is not None:
if any(arg is not None for arg in [date, time]):
raise PySparkValueError(
errorClass="CANNOT_SET_TOGETHER",
messageParameters={"arg_list": "years|months|days|hours|mins|secs and date|time"},
)
return _invoke_function_over_columns(
"try_make_timestamp_ntz",
cast("ColumnOrName", years),
cast("ColumnOrName", months),
cast("ColumnOrName", days),
cast("ColumnOrName", hours),
cast("ColumnOrName", mins),
cast("ColumnOrName", secs),
)
else:
if any(arg is not None for arg in [years, months, days, hours, mins, secs]):
raise PySparkValueError(
errorClass="CANNOT_SET_TOGETHER",
messageParameters={"arg_list": "years|months|days|hours|mins|secs and date|time"},
)
return _invoke_function_over_columns(
"try_make_timestamp_ntz", cast("ColumnOrName", date), cast("ColumnOrName", time)
)
@_try_remote_functions
def make_ym_interval(
years: Optional["ColumnOrName"] = None,
months: Optional["ColumnOrName"] = None,
) -> Column:
"""
Make year-month interval from years, months.
.. versionadded:: 3.5.0
Parameters
----------
years : :class:`~pyspark.sql.Column` or column name, optional
The number of years, positive or negative
months : :class:`~pyspark.sql.Column` or column name, optional
The number of months, positive or negative
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a year-month interval.
See Also
--------
:meth:`pyspark.sql.functions.make_interval`
:meth:`pyspark.sql.functions.make_dt_interval`
:meth:`pyspark.sql.functions.try_make_interval`
Examples
--------
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
Example 1: Make year-month interval from years, months.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12]], ['year', 'month'])
>>> df.select('*', sf.make_ym_interval('year', df.month)).show(truncate=False)
+----+-----+-------------------------------+
|year|month|make_ym_interval(year, month) |
+----+-----+-------------------------------+
|2014|12 |INTERVAL '2015-0' YEAR TO MONTH|
+----+-----+-------------------------------+
Example 2: Make year-month interval from years.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([[2014, 12]], ['year', 'month'])
>>> df.select('*', sf.make_ym_interval(df.year)).show(truncate=False)
+----+-----+-------------------------------+
|year|month|make_ym_interval(year, 0) |
+----+-----+-------------------------------+
|2014|12 |INTERVAL '2014-0' YEAR TO MONTH|
+----+-----+-------------------------------+
Example 3: Make empty interval.
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.make_ym_interval()).show(truncate=False)
+----------------------------+
|make_ym_interval(0, 0) |
+----------------------------+
|INTERVAL '0-0' YEAR TO MONTH|
+----------------------------+
>>> spark.conf.unset("spark.sql.session.timeZone")
"""
_years = lit(0) if years is None else years
_months = lit(0) if months is None else months
return _invoke_function_over_columns("make_ym_interval", _years, _months)
@_try_remote_functions
def bucket(numBuckets: Union[Column, int], col: "ColumnOrName") -> Column:
"""
Partition transform function: A transform for any type that partitions
by a hash of the input column.
.. versionadded:: 3.1.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. deprecated:: 4.0.0
Use :func:`partitioning.bucket` instead.
Examples
--------
>>> df.writeTo("catalog.db.table").partitionedBy( # doctest: +SKIP
... bucket(42, "ts")
... ).createOrReplace()
Parameters
----------
numBuckets : :class:`~pyspark.sql.Column` or int
the number of buckets
col : :class:`~pyspark.sql.Column` or str
target date or timestamp column to work on.
Returns
-------
:class:`~pyspark.sql.Column`
data partitioned by given columns.
Notes
-----
This function can be used only in combination with
:py:meth:`~pyspark.sql.readwriter.DataFrameWriterV2.partitionedBy`
method of the `DataFrameWriterV2`.
"""
from pyspark.sql.functions import partitioning
warnings.warn("Deprecated in 4.0.0, use partitioning.bucket instead.", FutureWarning)
return partitioning.bucket(numBuckets, col)
@_try_remote_functions
def call_udf(udfName: str, *cols: "ColumnOrName") -> Column:
"""
Call a user-defined function.
.. versionadded:: 3.4.0
Parameters
----------
udfName : str
name of the user defined function (UDF)
cols : :class:`~pyspark.sql.Column` or str
column names or :class:`~pyspark.sql.Column`\\s to be used in the UDF
Returns
-------
:class:`~pyspark.sql.Column`
result of executed udf.
Examples
--------
>>> from pyspark.sql.functions import call_udf, col
>>> from pyspark.sql.types import IntegerType, StringType
>>> df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "c")],["id", "name"])
>>> _ = spark.udf.register("intX2", lambda i: i * 2, IntegerType())
>>> df.select(call_udf("intX2", "id")).show()
+---------+
|intX2(id)|
+---------+
| 2|
| 4|
| 6|
+---------+
>>> _ = spark.udf.register("strX2", lambda s: s * 2, StringType())
>>> df.select(call_udf("strX2", col("name"))).show()
+-----------+
|strX2(name)|
+-----------+
| aa|
| bb|
| cc|
+-----------+
"""
from pyspark.sql.classic.column import _to_seq, _to_java_column
sc = _get_active_spark_context()
return _invoke_function("call_udf", udfName, _to_seq(sc, cols, _to_java_column))
@_try_remote_functions
def call_function(funcName: str, *cols: "ColumnOrName") -> Column:
"""
Call a SQL function.
.. versionadded:: 3.5.0
Parameters
----------
funcName : str
function name that follows the SQL identifier syntax (can be quoted, can be qualified)
cols : :class:`~pyspark.sql.Column` or str
column names or :class:`~pyspark.sql.Column`\\s to be used in the function
Returns
-------
:class:`~pyspark.sql.Column`
result of executed function.
Examples
--------
>>> from pyspark.sql.functions import call_udf, col
>>> from pyspark.sql.types import IntegerType, StringType
>>> df = spark.createDataFrame([(1, "a"),(2, "b"), (3, "c")],["id", "name"])
>>> _ = spark.udf.register("intX2", lambda i: i * 2, IntegerType())
>>> df.select(call_function("intX2", "id")).show()
+---------+
|intX2(id)|
+---------+
| 2|
| 4|
| 6|
+---------+
>>> _ = spark.udf.register("strX2", lambda s: s * 2, StringType())
>>> df.select(call_function("strX2", col("name"))).show()
+-----------+
|strX2(name)|
+-----------+
| aa|
| bb|
| cc|
+-----------+
>>> df.select(call_function("avg", col("id"))).show()
+-------+
|avg(id)|
+-------+
| 2.0|
+-------+
>>> _ = spark.sql("CREATE FUNCTION custom_avg AS 'test.org.apache.spark.sql.MyDoubleAvg'")
... # doctest: +SKIP
>>> df.select(call_function("custom_avg", col("id"))).show()
... # doctest: +SKIP
+------------------------------------+
|spark_catalog.default.custom_avg(id)|
+------------------------------------+
| 102.0|
+------------------------------------+
>>> df.select(call_function("spark_catalog.default.custom_avg", col("id"))).show()
... # doctest: +SKIP
+------------------------------------+
|spark_catalog.default.custom_avg(id)|
+------------------------------------+
| 102.0|
+------------------------------------+
"""
from pyspark.sql.classic.column import _to_seq, _to_java_column
sc = _get_active_spark_context()
return _invoke_function("call_function", funcName, _to_seq(sc, cols, _to_java_column))
@_try_remote_functions
def unwrap_udt(col: "ColumnOrName") -> Column:
"""
Unwrap UDT data type column into its underlying type.
.. versionadded:: 3.4.0
Notes
-----
Supports Spark Connect.
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("unwrap_udt", _to_java_column(col))
@_try_remote_functions
def hll_sketch_agg(
col: "ColumnOrName",
lgConfigK: Optional[Union[int, Column]] = None,
) -> Column:
"""
Aggregate function: returns the updatable binary representation of the Datasketches
HllSketch configured with lgConfigK arg.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
lgConfigK : :class:`~pyspark.sql.Column` or int, optional
The log-base-2 of K, where K is the number of buckets or slots for the HllSketch
Returns
-------
:class:`~pyspark.sql.Column`
The binary representation of the HllSketch.
See Also
--------
:meth:`pyspark.sql.functions.hll_union`
:meth:`pyspark.sql.functions.hll_union_agg`
:meth:`pyspark.sql.functions.hll_sketch_estimate`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(sf.hll_sketch_estimate(sf.hll_sketch_agg("value"))).show()
+----------------------------------------------+
|hll_sketch_estimate(hll_sketch_agg(value, 12))|
+----------------------------------------------+
| 3|
+----------------------------------------------+
>>> df.agg(sf.hll_sketch_estimate(sf.hll_sketch_agg("value", 12))).show()
+----------------------------------------------+
|hll_sketch_estimate(hll_sketch_agg(value, 12))|
+----------------------------------------------+
| 3|
+----------------------------------------------+
"""
if lgConfigK is None:
return _invoke_function_over_columns("hll_sketch_agg", col)
else:
return _invoke_function_over_columns("hll_sketch_agg", col, lit(lgConfigK))
@_try_remote_functions
def hll_union_agg(
col: "ColumnOrName",
allowDifferentLgConfigK: Optional[Union[bool, Column]] = None,
) -> Column:
"""
Aggregate function: returns the updatable binary representation of the Datasketches
HllSketch, generated by merging previously created Datasketches HllSketch instances
via a Datasketches Union instance. Throws an exception if sketches have different
lgConfigK values and allowDifferentLgConfigK is unset or set to false.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
allowDifferentLgConfigK : :class:`~pyspark.sql.Column` or bool, optional
Allow sketches with different lgConfigK values to be merged (defaults to false).
Returns
-------
:class:`~pyspark.sql.Column`
The binary representation of the merged HllSketch.
See Also
--------
:meth:`pyspark.sql.functions.hll_union`
:meth:`pyspark.sql.functions.hll_sketch_agg`
:meth:`pyspark.sql.functions.hll_sketch_estimate`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df1 = spark.createDataFrame([1,2,2,3], "INT")
>>> df1 = df1.agg(sf.hll_sketch_agg("value").alias("sketch"))
>>> df2 = spark.createDataFrame([4,5,5,6], "INT")
>>> df2 = df2.agg(sf.hll_sketch_agg("value").alias("sketch"))
>>> df3 = df1.union(df2)
>>> df3.agg(sf.hll_sketch_estimate(sf.hll_union_agg("sketch"))).show()
+-------------------------------------------------+
|hll_sketch_estimate(hll_union_agg(sketch, false))|
+-------------------------------------------------+
| 6|
+-------------------------------------------------+
>>> df3.agg(sf.hll_sketch_estimate(sf.hll_union_agg("sketch", False))).show()
+-------------------------------------------------+
|hll_sketch_estimate(hll_union_agg(sketch, false))|
+-------------------------------------------------+
| 6|
+-------------------------------------------------+
"""
if allowDifferentLgConfigK is None:
return _invoke_function_over_columns("hll_union_agg", col)
else:
return _invoke_function_over_columns("hll_union_agg", col, lit(allowDifferentLgConfigK))
@_try_remote_functions
def hll_sketch_estimate(col: "ColumnOrName") -> Column:
"""
Returns the estimated number of unique values given the binary representation
of a Datasketches HllSketch.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Returns
-------
:class:`~pyspark.sql.Column`
The estimated number of unique values for the HllSketch.
See Also
--------
:meth:`pyspark.sql.functions.hll_union`
:meth:`pyspark.sql.functions.hll_union_agg`
:meth:`pyspark.sql.functions.hll_sketch_agg`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(sf.hll_sketch_estimate(sf.hll_sketch_agg("value"))).show()
+----------------------------------------------+
|hll_sketch_estimate(hll_sketch_agg(value, 12))|
+----------------------------------------------+
| 3|
+----------------------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
return _invoke_function("hll_sketch_estimate", _to_java_column(col))
@_try_remote_functions
def hll_union(
col1: "ColumnOrName", col2: "ColumnOrName", allowDifferentLgConfigK: Optional[bool] = None
) -> Column:
"""
Merges two binary representations of Datasketches HllSketch objects, using a
Datasketches Union object. Throws an exception if sketches have different
lgConfigK values and allowDifferentLgConfigK is unset or set to false.
.. versionadded:: 3.5.0
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
col2 : :class:`~pyspark.sql.Column` or column name
allowDifferentLgConfigK : bool, optional
Allow sketches with different lgConfigK values to be merged (defaults to false).
Returns
-------
:class:`~pyspark.sql.Column`
The binary representation of the merged HllSketch.
See Also
--------
:meth:`pyspark.sql.functions.hll_union_agg`
:meth:`pyspark.sql.functions.hll_sketch_agg`
:meth:`pyspark.sql.functions.hll_sketch_estimate`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,4),(2,5),(2,5),(3,6)], "struct<v1:int,v2:int>")
>>> df = df.agg(
... sf.hll_sketch_agg("v1").alias("sketch1"),
... sf.hll_sketch_agg("v2").alias("sketch2")
... )
>>> df.select(sf.hll_sketch_estimate(sf.hll_union(df.sketch1, "sketch2"))).show()
+-------------------------------------------------------+
|hll_sketch_estimate(hll_union(sketch1, sketch2, false))|
+-------------------------------------------------------+
| 6|
+-------------------------------------------------------+
"""
from pyspark.sql.classic.column import _to_java_column
if allowDifferentLgConfigK is not None:
return _invoke_function(
"hll_union",
_to_java_column(col1),
_to_java_column(col2),
_enum_to_value(allowDifferentLgConfigK),
)
else:
return _invoke_function("hll_union", _to_java_column(col1), _to_java_column(col2))
@_try_remote_functions
def theta_sketch_agg(
col: "ColumnOrName",
lgNomEntries: Optional[Union[int, Column]] = None,
) -> Column:
"""
Aggregate function: returns the compact binary representation of the Datasketches
ThetaSketch with the values in the input column configured with lgNomEntries nominal entries.
.. versionadded:: 4.1.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
lgNomEntries : :class:`~pyspark.sql.Column` or int, optional
The log-base-2 of nominal entries, where nominal entries is the size of the sketch
(must be between 4 and 26, defaults to 12)
Returns
-------
:class:`~pyspark.sql.Column`
The binary representation of the ThetaSketch.
See Also
--------
:meth:`pyspark.sql.functions.theta_union`
:meth:`pyspark.sql.functions.theta_intersection`
:meth:`pyspark.sql.functions.theta_difference`
:meth:`pyspark.sql.functions.theta_union_agg`
:meth:`pyspark.sql.functions.theta_intersection_agg`
:meth:`pyspark.sql.functions.theta_sketch_estimate`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(sf.theta_sketch_estimate(sf.theta_sketch_agg("value"))).show()
+--------------------------------------------------+
|theta_sketch_estimate(theta_sketch_agg(value, 12))|
+--------------------------------------------------+
| 3|
+--------------------------------------------------+
>>> df.agg(sf.theta_sketch_estimate(sf.theta_sketch_agg("value", 15))).show()
+--------------------------------------------------+
|theta_sketch_estimate(theta_sketch_agg(value, 15))|
+--------------------------------------------------+
| 3|
+--------------------------------------------------+
"""
fn = "theta_sketch_agg"
if lgNomEntries is None:
return _invoke_function_over_columns(fn, col)
else:
return _invoke_function_over_columns(fn, col, lit(lgNomEntries))
@_try_remote_functions
def theta_union_agg(
col: "ColumnOrName",
lgNomEntries: Optional[Union[int, Column]] = None,
) -> Column:
"""
Aggregate function: returns the compact binary representation of the Datasketches
ThetaSketch that is the union of the Theta sketches in the input column.
.. versionadded:: 4.1.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
lgNomEntries : :class:`~pyspark.sql.Column` or int, optional
The log-base-2 of nominal entries for the union operation
(must be between 4 and 26, defaults to 12)
Returns
-------
:class:`~pyspark.sql.Column`
The binary representation of the merged ThetaSketch.
See Also
--------
:meth:`pyspark.sql.functions.theta_union`
:meth:`pyspark.sql.functions.theta_sketch_agg`
:meth:`pyspark.sql.functions.theta_sketch_estimate`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df1 = spark.createDataFrame([1,2,2,3], "INT")
>>> df1 = df1.agg(sf.theta_sketch_agg("value").alias("sketch"))
>>> df2 = spark.createDataFrame([4,5,5,6], "INT")
>>> df2 = df2.agg(sf.theta_sketch_agg("value").alias("sketch"))
>>> df3 = df1.union(df2)
>>> df3.agg(sf.theta_sketch_estimate(sf.theta_union_agg("sketch"))).show()
+--------------------------------------------------+
|theta_sketch_estimate(theta_union_agg(sketch, 12))|
+--------------------------------------------------+
| 6|
+--------------------------------------------------+
"""
fn = "theta_union_agg"
if lgNomEntries is None:
return _invoke_function_over_columns(fn, col)
else:
return _invoke_function_over_columns(fn, col, lit(lgNomEntries))
@_try_remote_functions
def theta_intersection_agg(col: "ColumnOrName") -> Column:
"""
Aggregate function: returns the compact binary representation of the Datasketches
ThetaSketch that is the intersection of the Theta sketches in the input column
.. versionadded:: 4.1.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Returns
-------
:class:`~pyspark.sql.Column`
The binary representation of the intersected ThetaSketch.
See Also
--------
:meth:`pyspark.sql.functions.theta_intersection`
:meth:`pyspark.sql.functions.theta_sketch_agg`
:meth:`pyspark.sql.functions.theta_sketch_estimate`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df1 = spark.createDataFrame([1,2,2,3], "INT")
>>> df1 = df1.agg(sf.theta_sketch_agg("value").alias("sketch"))
>>> df2 = spark.createDataFrame([2,3,3,4], "INT")
>>> df2 = df2.agg(sf.theta_sketch_agg("value").alias("sketch"))
>>> df3 = df1.union(df2)
>>> df3.agg(sf.theta_sketch_estimate(sf.theta_intersection_agg("sketch"))).show()
+-----------------------------------------------------+
|theta_sketch_estimate(theta_intersection_agg(sketch))|
+-----------------------------------------------------+
| 2|
+-----------------------------------------------------+
"""
fn = "theta_intersection_agg"
return _invoke_function_over_columns(fn, col)
@_try_remote_functions
def theta_sketch_estimate(col: "ColumnOrName") -> Column:
"""
Returns the estimated number of unique values given the binary representation
of a Datasketches ThetaSketch.
.. versionadded:: 4.1.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Returns
-------
:class:`~pyspark.sql.Column`
The estimated number of unique values for the ThetaSketch.
See Also
--------
:meth:`pyspark.sql.functions.theta_union`
:meth:`pyspark.sql.functions.theta_intersection`
:meth:`pyspark.sql.functions.theta_difference`
:meth:`pyspark.sql.functions.theta_union_agg`
:meth:`pyspark.sql.functions.theta_intersection_agg`
:meth:`pyspark.sql.functions.theta_sketch_agg`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([1,2,2,3], "INT")
>>> df.agg(sf.theta_sketch_estimate(sf.theta_sketch_agg("value"))).show()
+--------------------------------------------------+
|theta_sketch_estimate(theta_sketch_agg(value, 12))|
+--------------------------------------------------+
| 3|
+--------------------------------------------------+
"""
fn = "theta_sketch_estimate"
return _invoke_function_over_columns(fn, col)
@_try_remote_functions
def theta_union(
col1: "ColumnOrName", col2: "ColumnOrName", lgNomEntries: Optional[Union[int, Column]] = None
) -> Column:
"""
Merges two binary representations of Datasketches ThetaSketch objects, using a
Datasketches Union object.
.. versionadded:: 4.1.0
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
col2 : :class:`~pyspark.sql.Column` or column name
lgNomEntries : :class:`~pyspark.sql.Column` or int, optional
The log-base-2 of nominal entries for the union operation
(must be between 4 and 26, defaults to 12)
Returns
-------
:class:`~pyspark.sql.Column`
The binary representation of the merged ThetaSketch.
See Also
--------
:meth:`pyspark.sql.functions.theta_union_agg`
:meth:`pyspark.sql.functions.theta_sketch_agg`
:meth:`pyspark.sql.functions.theta_sketch_estimate`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,4),(2,5),(2,5),(3,6)], "struct<v1:int,v2:int>")
>>> df = df.agg(
... sf.theta_sketch_agg("v1").alias("sketch1"),
... sf.theta_sketch_agg("v2").alias("sketch2")
... )
>>> df.select(sf.theta_sketch_estimate(sf.theta_union(df.sketch1, "sketch2"))).show()
+--------------------------------------------------------+
|theta_sketch_estimate(theta_union(sketch1, sketch2, 12))|
+--------------------------------------------------------+
| 6|
+--------------------------------------------------------+
"""
fn = "theta_union"
if lgNomEntries is not None:
return _invoke_function_over_columns(
fn,
col1,
col2,
lit(lgNomEntries),
)
else:
return _invoke_function_over_columns(fn, col1, col2)
@_try_remote_functions
def theta_intersection(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""
Returns the intersection of two binary representations of Datasketches ThetaSketch
objects, using a Datasketches Intersection object.
.. versionadded:: 4.1.0
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
col2 : :class:`~pyspark.sql.Column` or column name
Returns
-------
:class:`~pyspark.sql.Column`
The binary representation of the intersected ThetaSketch.
See Also
--------
:meth:`pyspark.sql.functions.theta_intersection_agg`
:meth:`pyspark.sql.functions.theta_sketch_agg`
:meth:`pyspark.sql.functions.theta_sketch_estimate`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,1),(2,2),(3,2),(3,3)], "struct<v1:int,v2:int>")
>>> df = df.agg(
... sf.theta_sketch_agg("v1").alias("sketch1"),
... sf.theta_sketch_agg("v2").alias("sketch2")
... )
>>> df.select(sf.theta_sketch_estimate(sf.theta_intersection(df.sketch1, "sketch2"))).show()
+-----------------------------------------------------------+
|theta_sketch_estimate(theta_intersection(sketch1, sketch2))|
+-----------------------------------------------------------+
| 3|
+-----------------------------------------------------------+
"""
fn = "theta_intersection"
return _invoke_function_over_columns(fn, col1, col2)
@_try_remote_functions
def theta_difference(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""
Returns the set difference of two binary representations of Datasketches ThetaSketch
objects (elements in first sketch but not in second), using a Datasketches ANotB object.
.. versionadded:: 4.1.0
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
col2 : :class:`~pyspark.sql.Column` or column name
Returns
-------
:class:`~pyspark.sql.Column`
The binary representation of the difference ThetaSketch.
See Also
--------
:meth:`pyspark.sql.functions.theta_union`
:meth:`pyspark.sql.functions.theta_intersection`
:meth:`pyspark.sql.functions.theta_sketch_agg`
:meth:`pyspark.sql.functions.theta_sketch_estimate`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,4),(2,4),(3,5),(4,5)], "struct<v1:int,v2:int>")
>>> df = df.agg(
... sf.theta_sketch_agg("v1").alias("sketch1"),
... sf.theta_sketch_agg("v2").alias("sketch2")
... )
>>> df.select(sf.theta_sketch_estimate(sf.theta_difference(df.sketch1, "sketch2"))).show()
+---------------------------------------------------------+
|theta_sketch_estimate(theta_difference(sketch1, sketch2))|
+---------------------------------------------------------+
| 3|
+---------------------------------------------------------+
"""
fn = "theta_difference"
return _invoke_function_over_columns(fn, col1, col2)
# ---------------------- Predicates functions ------------------------------
@_try_remote_functions
def ifnull(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""
Returns `col2` if `col1` is null, or `col1` otherwise.
.. versionadded:: 3.5.0
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or str
col2 : :class:`~pyspark.sql.Column` or str
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(None,), (1,)], ["e"])
>>> df.select(sf.ifnull(df.e, sf.lit(8))).show()
+------------+
|ifnull(e, 8)|
+------------+
| 8|
| 1|
+------------+
"""
return _invoke_function_over_columns("ifnull", col1, col2)
@_try_remote_functions
def isnotnull(col: "ColumnOrName") -> Column:
"""
Returns true if `col` is not null, or false otherwise.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(None,), (1,)], ["e"])
>>> df.select('*', sf.isnotnull(df.e)).show()
+----+---------------+
| e|(e IS NOT NULL)|
+----+---------------+
|NULL| false|
| 1| true|
+----+---------------+
>>> df.select('*', sf.isnotnull('e')).show()
+----+---------------+
| e|(e IS NOT NULL)|
+----+---------------+
|NULL| false|
| 1| true|
+----+---------------+
"""
return _invoke_function_over_columns("isnotnull", col)
@_try_remote_functions
def equal_null(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""
Returns same result as the EQUAL(=) operator for non-null operands,
but returns true if both are null, false if one of them is null.
.. versionadded:: 3.5.0
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
col2 : :class:`~pyspark.sql.Column` or column name
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(None, None,), (1, 9,)], ["a", "b"])
>>> df.select('*', sf.equal_null(df.a, df.b)).show()
+----+----+----------------+
| a| b|equal_null(a, b)|
+----+----+----------------+
|NULL|NULL| true|
| 1| 9| false|
+----+----+----------------+
>>> df.select('*', sf.equal_null('a', 'b')).show()
+----+----+----------------+
| a| b|equal_null(a, b)|
+----+----+----------------+
|NULL|NULL| true|
| 1| 9| false|
+----+----+----------------+
"""
return _invoke_function_over_columns("equal_null", col1, col2)
@_try_remote_functions
def nullif(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""
Returns null if `col1` equals to `col2`, or `col1` otherwise.
.. versionadded:: 3.5.0
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
col2 : :class:`~pyspark.sql.Column` or column name
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(None, None,), (1, 9,)], ["a", "b"])
>>> df.select('*', sf.nullif(df.a, df.b)).show()
+----+----+------------+
| a| b|nullif(a, b)|
+----+----+------------+
|NULL|NULL| NULL|
| 1| 9| 1|
+----+----+------------+
>>> df.select('*', sf.nullif('a', 'b')).show()
+----+----+------------+
| a| b|nullif(a, b)|
+----+----+------------+
|NULL|NULL| NULL|
| 1| 9| 1|
+----+----+------------+
"""
return _invoke_function_over_columns("nullif", col1, col2)
@_try_remote_functions
def nullifzero(col: "ColumnOrName") -> Column:
"""
Returns null if `col` is equal to zero, or `col` otherwise.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(0,), (1,)], ["a"])
>>> df.select('*', sf.nullifzero(df.a)).show()
+---+-------------+
| a|nullifzero(a)|
+---+-------------+
| 0| NULL|
| 1| 1|
+---+-------------+
>>> df.select('*', sf.nullifzero('a')).show()
+---+-------------+
| a|nullifzero(a)|
+---+-------------+
| 0| NULL|
| 1| 1|
+---+-------------+
"""
return _invoke_function_over_columns("nullifzero", col)
@_try_remote_functions
def nvl(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
"""
Returns `col2` if `col1` is null, or `col1` otherwise.
.. versionadded:: 3.5.0
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
col2 : :class:`~pyspark.sql.Column` or column name
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(None, 8,), (1, 9,)], ["a", "b"])
>>> df.select('*', sf.nvl(df.a, df.b)).show()
+----+---+---------+
| a| b|nvl(a, b)|
+----+---+---------+
|NULL| 8| 8|
| 1| 9| 1|
+----+---+---------+
>>> df.select('*', sf.nvl('a', 'b')).show()
+----+---+---------+
| a| b|nvl(a, b)|
+----+---+---------+
|NULL| 8| 8|
| 1| 9| 1|
+----+---+---------+
"""
return _invoke_function_over_columns("nvl", col1, col2)
@_try_remote_functions
def nvl2(col1: "ColumnOrName", col2: "ColumnOrName", col3: "ColumnOrName") -> Column:
"""
Returns `col2` if `col1` is not null, or `col3` otherwise.
.. versionadded:: 3.5.0
Parameters
----------
col1 : :class:`~pyspark.sql.Column` or column name
col2 : :class:`~pyspark.sql.Column` or column name
col3 : :class:`~pyspark.sql.Column` or column name
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(None, 8, 6,), (1, 9, 9,)], ["a", "b", "c"])
>>> df.select('*', sf.nvl2(df.a, df.b, df.c)).show()
+----+---+---+-------------+
| a| b| c|nvl2(a, b, c)|
+----+---+---+-------------+
|NULL| 8| 6| 6|
| 1| 9| 9| 9|
+----+---+---+-------------+
>>> df.select('*', sf.nvl2('a', 'b', 'c')).show()
+----+---+---+-------------+
| a| b| c|nvl2(a, b, c)|
+----+---+---+-------------+
|NULL| 8| 6| 6|
| 1| 9| 9| 9|
+----+---+---+-------------+
"""
return _invoke_function_over_columns("nvl2", col1, col2, col3)
@_try_remote_functions
def zeroifnull(col: "ColumnOrName") -> Column:
"""
Returns zero if `col` is null, or `col` otherwise.
.. versionadded:: 4.0.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(None,), (1,)], ["a"])
>>> df.select('*', sf.zeroifnull(df.a)).show()
+----+-------------+
| a|zeroifnull(a)|
+----+-------------+
|NULL| 0|
| 1| 1|
+----+-------------+
>>> df.select('*', sf.zeroifnull('a')).show()
+----+-------------+
| a|zeroifnull(a)|
+----+-------------+
|NULL| 0|
| 1| 1|
+----+-------------+
"""
return _invoke_function_over_columns("zeroifnull", col)
@_try_remote_functions
def aes_encrypt(
input: "ColumnOrName",
key: "ColumnOrName",
mode: Optional["ColumnOrName"] = None,
padding: Optional["ColumnOrName"] = None,
iv: Optional["ColumnOrName"] = None,
aad: Optional["ColumnOrName"] = None,
) -> Column:
"""
Returns an encrypted value of `input` using AES in given `mode` with the specified `padding`.
Key lengths of 16, 24 and 32 bits are supported. Supported combinations of (`mode`,
`padding`) are ('ECB', 'PKCS'), ('GCM', 'NONE') and ('CBC', 'PKCS'). Optional initialization
vectors (IVs) are only supported for CBC and GCM modes. These must be 16 bytes for CBC and 12
bytes for GCM. If not provided, a random vector will be generated and prepended to the
output. Optional additional authenticated data (AAD) is only supported for GCM. If provided
for encryption, the identical AAD value must be provided for decryption. The default mode is
GCM.
.. versionadded:: 3.5.0
Parameters
----------
input : :class:`~pyspark.sql.Column` or column name
The binary value to encrypt.
key : :class:`~pyspark.sql.Column` or column name
The passphrase to use to encrypt the data.
mode : :class:`~pyspark.sql.Column` or str, optional
Specifies which block cipher mode should be used to encrypt messages. Valid modes: ECB,
GCM, CBC.
padding : :class:`~pyspark.sql.Column` or column name, optional
Specifies how to pad messages whose length is not a multiple of the block size. Valid
values: PKCS, NONE, DEFAULT. The DEFAULT padding means PKCS for ECB, NONE for GCM and PKCS
for CBC.
iv : :class:`~pyspark.sql.Column` or column name, optional
Optional initialization vector. Only supported for CBC and GCM modes. Valid values: None or
"". 16-byte array for CBC mode. 12-byte array for GCM mode.
aad : :class:`~pyspark.sql.Column` or column name, optional
Optional additional authenticated data. Only supported for GCM mode. This can be any
free-form input and must be provided for both encryption and decryption.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains an encrypted value.
See Also
--------
:meth:`pyspark.sql.functions.aes_decrypt`
:meth:`pyspark.sql.functions.try_aes_decrypt`
Examples
--------
Example 1: Encrypt data with key, mode, padding, iv and aad.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "Spark", "abcdefghijklmnop12345678ABCDEFGH", "GCM", "DEFAULT",
... "000000000000000000000000", "This is an AAD mixed into the input",)],
... ["input", "key", "mode", "padding", "iv", "aad"]
... )
>>> df.select(sf.base64(sf.aes_encrypt(
... df.input, df.key, "mode", df.padding, sf.to_binary(df.iv, sf.lit("hex")), df.aad)
... )).show(truncate=False)
+-----------------------------------------------------------------------+
|base64(aes_encrypt(input, key, mode, padding, to_binary(iv, hex), aad))|
+-----------------------------------------------------------------------+
|AAAAAAAAAAAAAAAAQiYi+sTLm7KD9UcZ2nlRdYDe/PX4 |
+-----------------------------------------------------------------------+
Example 2: Encrypt data with key, mode, padding and iv.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "Spark", "abcdefghijklmnop12345678ABCDEFGH", "GCM", "DEFAULT",
... "000000000000000000000000", "This is an AAD mixed into the input",)],
... ["input", "key", "mode", "padding", "iv", "aad"]
... )
>>> df.select(sf.base64(sf.aes_encrypt(
... df.input, df.key, "mode", df.padding, sf.to_binary(df.iv, sf.lit("hex")))
... )).show(truncate=False)
+--------------------------------------------------------------------+
|base64(aes_encrypt(input, key, mode, padding, to_binary(iv, hex), ))|
+--------------------------------------------------------------------+
|AAAAAAAAAAAAAAAAQiYi+sRNYDAOTjdSEcYBFsAWPL1f |
+--------------------------------------------------------------------+
Example 3: Encrypt data with key, mode and padding.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "Spark SQL", "1234567890abcdef", "ECB", "PKCS",)],
... ["input", "key", "mode", "padding"]
... )
>>> df.select(sf.aes_decrypt(sf.aes_encrypt(df.input, df.key, "mode", df.padding),
... df.key, df.mode, df.padding
... ).cast("STRING")).show(truncate=False)
+---------------------------------------------------------------------------------------------+
|CAST(aes_decrypt(aes_encrypt(input, key, mode, padding, , ), key, mode, padding, ) AS STRING)|
+---------------------------------------------------------------------------------------------+
|Spark SQL |
+---------------------------------------------------------------------------------------------+
Example 4: Encrypt data with key and mode.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "Spark SQL", "0000111122223333", "ECB",)],
... ["input", "key", "mode"]
... )
>>> df.select(sf.aes_decrypt(sf.aes_encrypt(df.input, df.key, "mode"),
... df.key, df.mode
... ).cast("STRING")).show(truncate=False)
+---------------------------------------------------------------------------------------------+
|CAST(aes_decrypt(aes_encrypt(input, key, mode, DEFAULT, , ), key, mode, DEFAULT, ) AS STRING)|
+---------------------------------------------------------------------------------------------+
|Spark SQL |
+---------------------------------------------------------------------------------------------+
Example 5: Encrypt data with key.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "Spark SQL", "abcdefghijklmnop",)],
... ["input", "key"]
... )
>>> df.select(sf.aes_decrypt(
... sf.unbase64(sf.base64(sf.aes_encrypt(df.input, df.key))), df.key
... ).cast("STRING")).show(truncate=False)
+-------------------------------------------------------------------------------------------------------------+
|CAST(aes_decrypt(unbase64(base64(aes_encrypt(input, key, GCM, DEFAULT, , ))), key, GCM, DEFAULT, ) AS STRING)|
+-------------------------------------------------------------------------------------------------------------+
|Spark SQL |
+-------------------------------------------------------------------------------------------------------------+
""" # noqa: E501
_mode = lit("GCM") if mode is None else mode
_padding = lit("DEFAULT") if padding is None else padding
_iv = lit("") if iv is None else iv
_aad = lit("") if aad is None else aad
return _invoke_function_over_columns("aes_encrypt", input, key, _mode, _padding, _iv, _aad)
@_try_remote_functions
def aes_decrypt(
input: "ColumnOrName",
key: "ColumnOrName",
mode: Optional["ColumnOrName"] = None,
padding: Optional["ColumnOrName"] = None,
aad: Optional["ColumnOrName"] = None,
) -> Column:
"""
Returns a decrypted value of `input` using AES in `mode` with `padding`. Key lengths of 16,
24 and 32 bits are supported. Supported combinations of (`mode`, `padding`) are ('ECB',
'PKCS'), ('GCM', 'NONE') and ('CBC', 'PKCS'). Optional additional authenticated data (AAD) is
only supported for GCM. If provided for encryption, the identical AAD value must be provided
for decryption. The default mode is GCM.
.. versionadded:: 3.5.0
Parameters
----------
input : :class:`~pyspark.sql.Column` or column name
The binary value to decrypt.
key : :class:`~pyspark.sql.Column` or column name
The passphrase to use to decrypt the data.
mode : :class:`~pyspark.sql.Column` or column name, optional
Specifies which block cipher mode should be used to decrypt messages. Valid modes: ECB,
GCM, CBC.
padding : :class:`~pyspark.sql.Column` or column name, optional
Specifies how to pad messages whose length is not a multiple of the block size. Valid
values: PKCS, NONE, DEFAULT. The DEFAULT padding means PKCS for ECB, NONE for GCM and PKCS
for CBC.
aad : :class:`~pyspark.sql.Column` or column name, optional
Optional additional authenticated data. Only supported for GCM mode. This can be any
free-form input and must be provided for both encryption and decryption.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a decrypted value.
See Also
--------
:meth:`pyspark.sql.functions.aes_encrypt`
:meth:`pyspark.sql.functions.try_aes_decrypt`
Examples
--------
Example 1: Decrypt data with key, mode, padding and aad.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "AAAAAAAAAAAAAAAAQiYi+sTLm7KD9UcZ2nlRdYDe/PX4",
... "abcdefghijklmnop12345678ABCDEFGH", "GCM", "DEFAULT",
... "This is an AAD mixed into the input",)],
... ["input", "key", "mode", "padding", "aad"]
... )
>>> df.select(sf.aes_decrypt(
... sf.unbase64(df.input), df.key, "mode", df.padding, df.aad
... ).cast("STRING")).show(truncate=False)
+---------------------------------------------------------------------+
|CAST(aes_decrypt(unbase64(input), key, mode, padding, aad) AS STRING)|
+---------------------------------------------------------------------+
|Spark |
+---------------------------------------------------------------------+
Example 2: Decrypt data with key, mode and padding.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "AAAAAAAAAAAAAAAAAAAAAPSd4mWyMZ5mhvjiAPQJnfg=",
... "abcdefghijklmnop12345678ABCDEFGH", "CBC", "DEFAULT",)],
... ["input", "key", "mode", "padding"]
... )
>>> df.select(sf.aes_decrypt(
... sf.unbase64(df.input), df.key, "mode", df.padding
... ).cast("STRING")).show(truncate=False)
+------------------------------------------------------------------+
|CAST(aes_decrypt(unbase64(input), key, mode, padding, ) AS STRING)|
+------------------------------------------------------------------+
|Spark |
+------------------------------------------------------------------+
Example 3: Decrypt data with key and mode.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "AAAAAAAAAAAAAAAAAAAAAPSd4mWyMZ5mhvjiAPQJnfg=",
... "abcdefghijklmnop12345678ABCDEFGH", "CBC", "DEFAULT",)],
... ["input", "key", "mode", "padding"]
... )
>>> df.select(sf.aes_decrypt(
... sf.unbase64(df.input), df.key, "mode"
... ).cast("STRING")).show(truncate=False)
+------------------------------------------------------------------+
|CAST(aes_decrypt(unbase64(input), key, mode, DEFAULT, ) AS STRING)|
+------------------------------------------------------------------+
|Spark |
+------------------------------------------------------------------+
Example 4: Decrypt data with key.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "83F16B2AA704794132802D248E6BFD4E380078182D1544813898AC97E709B28A94",
... "0000111122223333",)],
... ["input", "key"]
... )
>>> df.select(sf.aes_decrypt(
... sf.unhex(df.input), df.key
... ).cast("STRING")).show(truncate=False)
+--------------------------------------------------------------+
|CAST(aes_decrypt(unhex(input), key, GCM, DEFAULT, ) AS STRING)|
+--------------------------------------------------------------+
|Spark |
+--------------------------------------------------------------+
"""
_mode = lit("GCM") if mode is None else mode
_padding = lit("DEFAULT") if padding is None else padding
_aad = lit("") if aad is None else aad
return _invoke_function_over_columns("aes_decrypt", input, key, _mode, _padding, _aad)
@_try_remote_functions
def try_aes_decrypt(
input: "ColumnOrName",
key: "ColumnOrName",
mode: Optional["ColumnOrName"] = None,
padding: Optional["ColumnOrName"] = None,
aad: Optional["ColumnOrName"] = None,
) -> Column:
"""
This is a special version of `aes_decrypt` that performs the same operation,
but returns a NULL value instead of raising an error if the decryption cannot be performed.
Returns a decrypted value of `input` using AES in `mode` with `padding`. Key lengths of 16,
24 and 32 bits are supported. Supported combinations of (`mode`, `padding`) are ('ECB',
'PKCS'), ('GCM', 'NONE') and ('CBC', 'PKCS'). Optional additional authenticated data (AAD) is
only supported for GCM. If provided for encryption, the identical AAD value must be provided
for decryption. The default mode is GCM.
.. versionadded:: 3.5.0
Parameters
----------
input : :class:`~pyspark.sql.Column` or column name
The binary value to decrypt.
key : :class:`~pyspark.sql.Column` or column name
The passphrase to use to decrypt the data.
mode : :class:`~pyspark.sql.Column` or column name, optional
Specifies which block cipher mode should be used to decrypt messages. Valid modes: ECB,
GCM, CBC.
padding : :class:`~pyspark.sql.Column` or column name, optional
Specifies how to pad messages whose length is not a multiple of the block size. Valid
values: PKCS, NONE, DEFAULT. The DEFAULT padding means PKCS for ECB, NONE for GCM and PKCS
for CBC.
aad : :class:`~pyspark.sql.Column` or column name, optional
Optional additional authenticated data. Only supported for GCM mode. This can be any
free-form input and must be provided for both encryption and decryption.
Returns
-------
:class:`~pyspark.sql.Column`
A new column that contains a decrypted value or a NULL value.
See Also
--------
:meth:`pyspark.sql.functions.aes_encrypt`
:meth:`pyspark.sql.functions.aes_decrypt`
Examples
--------
Example 1: Decrypt data with key, mode, padding and aad.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "AAAAAAAAAAAAAAAAQiYi+sTLm7KD9UcZ2nlRdYDe/PX4",
... "abcdefghijklmnop12345678ABCDEFGH", "GCM", "DEFAULT",
... "This is an AAD mixed into the input",)],
... ["input", "key", "mode", "padding", "aad"]
... )
>>> df.select(sf.try_aes_decrypt(
... sf.unbase64(df.input), df.key, "mode", df.padding, df.aad
... ).cast("STRING")).show(truncate=False)
+-------------------------------------------------------------------------+
|CAST(try_aes_decrypt(unbase64(input), key, mode, padding, aad) AS STRING)|
+-------------------------------------------------------------------------+
|Spark |
+-------------------------------------------------------------------------+
Example 2: Failed to decrypt data with key, mode, padding and aad.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "AAAAAAAAAAAAAAAAQiYi+sTLm7KD9UcZ2nlRdYDe/PX4",
... "abcdefghijklmnop12345678ABCDEFGH", "CBC", "DEFAULT",
... "This is an AAD mixed into the input",)],
... ["input", "key", "mode", "padding", "aad"]
... )
>>> df.select(sf.try_aes_decrypt(
... sf.unbase64(df.input), df.key, "mode", df.padding, df.aad
... ).cast("STRING")).show(truncate=False)
+-------------------------------------------------------------------------+
|CAST(try_aes_decrypt(unbase64(input), key, mode, padding, aad) AS STRING)|
+-------------------------------------------------------------------------+
|NULL |
+-------------------------------------------------------------------------+
Example 3: Decrypt data with key, mode and padding.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "AAAAAAAAAAAAAAAAAAAAAPSd4mWyMZ5mhvjiAPQJnfg=",
... "abcdefghijklmnop12345678ABCDEFGH", "CBC", "DEFAULT",)],
... ["input", "key", "mode", "padding"]
... )
>>> df.select(sf.try_aes_decrypt(
... sf.unbase64(df.input), df.key, "mode", df.padding
... ).cast("STRING")).show(truncate=False)
+----------------------------------------------------------------------+
|CAST(try_aes_decrypt(unbase64(input), key, mode, padding, ) AS STRING)|
+----------------------------------------------------------------------+
|Spark |
+----------------------------------------------------------------------+
Example 4: Decrypt data with key and mode.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "AAAAAAAAAAAAAAAAAAAAAPSd4mWyMZ5mhvjiAPQJnfg=",
... "abcdefghijklmnop12345678ABCDEFGH", "CBC", "DEFAULT",)],
... ["input", "key", "mode", "padding"]
... )
>>> df.select(sf.try_aes_decrypt(
... sf.unbase64(df.input), df.key, "mode"
... ).cast("STRING")).show(truncate=False)
+----------------------------------------------------------------------+
|CAST(try_aes_decrypt(unbase64(input), key, mode, DEFAULT, ) AS STRING)|
+----------------------------------------------------------------------+
|Spark |
+----------------------------------------------------------------------+
Example 5: Decrypt data with key.
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([(
... "83F16B2AA704794132802D248E6BFD4E380078182D1544813898AC97E709B28A94",
... "0000111122223333",)],
... ["input", "key"]
... )
>>> df.select(sf.try_aes_decrypt(
... sf.unhex(df.input), df.key
... ).cast("STRING")).show(truncate=False)
+------------------------------------------------------------------+
|CAST(try_aes_decrypt(unhex(input), key, GCM, DEFAULT, ) AS STRING)|
+------------------------------------------------------------------+
|Spark |
+------------------------------------------------------------------+
"""
_mode = lit("GCM") if mode is None else mode
_padding = lit("DEFAULT") if padding is None else padding
_aad = lit("") if aad is None else aad
return _invoke_function_over_columns("try_aes_decrypt", input, key, _mode, _padding, _aad)
@_try_remote_functions
def sha(col: "ColumnOrName") -> Column:
"""
Returns a sha1 hash value as a hex string of the `col`.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
See Also
--------
:meth:`pyspark.sql.functions.sha1`
:meth:`pyspark.sql.functions.sha2`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(sf.sha(sf.lit("Spark"))).show()
+--------------------+
| sha(Spark)|
+--------------------+
|85f5955f4b27a9a4c...|
+--------------------+
"""
return _invoke_function_over_columns("sha", col)
@_try_remote_functions
def input_file_block_length() -> Column:
"""
Returns the length of the block being read, or -1 if not available.
.. versionadded:: 3.5.0
See Also
--------
:meth:`pyspark.sql.functions.input_file_name`
:meth:`pyspark.sql.functions.input_file_block_start`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.read.text("python/test_support/sql/ages_newlines.csv", lineSep=",")
>>> df.select(sf.input_file_block_length()).show()
+-------------------------+
|input_file_block_length()|
+-------------------------+
| 87|
| 87|
| 87|
| 87|
| 87|
| 87|
| 87|
| 87|
+-------------------------+
"""
return _invoke_function_over_columns("input_file_block_length")
@_try_remote_functions
def input_file_block_start() -> Column:
"""
Returns the start offset of the block being read, or -1 if not available.
.. versionadded:: 3.5.0
See Also
--------
:meth:`pyspark.sql.functions.input_file_name`
:meth:`pyspark.sql.functions.input_file_block_length`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.read.text("python/test_support/sql/ages_newlines.csv", lineSep=",")
>>> df.select(sf.input_file_block_start()).show()
+------------------------+
|input_file_block_start()|
+------------------------+
| 0|
| 0|
| 0|
| 0|
| 0|
| 0|
| 0|
| 0|
+------------------------+
"""
return _invoke_function_over_columns("input_file_block_start")
@_try_remote_functions
def reflect(*cols: "ColumnOrName") -> Column:
"""
Calls a method with reflection.
.. versionadded:: 3.5.0
Parameters
----------
cols : :class:`~pyspark.sql.Column` or column name
the first element should be a Column representing literal string for the class name,
and the second element should be a Column representing literal string for the method name,
and the remaining are input arguments (Columns or column names) to the Java method.
See Also
--------
:meth:`pyspark.sql.functions.java_method`
:meth:`pyspark.sql.functions.try_reflect`
Examples
--------
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('a5cf6c42-0c85-418f-af6c-3e4e5b1328f2',)], ['a'])
>>> df.select(
... sf.reflect(sf.lit('java.util.UUID'), sf.lit('fromString'), 'a')
... ).show(truncate=False)
+--------------------------------------+
|reflect(java.util.UUID, fromString, a)|
+--------------------------------------+
|a5cf6c42-0c85-418f-af6c-3e4e5b1328f2 |
+--------------------------------------+
"""
return _invoke_function_over_seq_of_columns("reflect", cols)
@_try_remote_functions
def java_method(*cols: "ColumnOrName") -> Column:
"""
Calls a method with reflection.
.. versionadded:: 3.5.0
Parameters
----------
cols : :class:`~pyspark.sql.Column` or column name
the first element should be a Column representing literal string for the class name,
and the second element should be a Column representing literal string for the method name,
and the remaining are input arguments (Columns or column names) to the Java method.
See Also
--------
:meth:`pyspark.sql.functions.reflect`
:meth:`pyspark.sql.functions.try_reflect`
Examples
--------
Example 1: Reflecting a method call with a column argument
>>> import pyspark.sql.functions as sf
>>> spark.range(1).select(
... sf.java_method(
... sf.lit("java.util.UUID"),
... sf.lit("fromString"),
... sf.lit("a5cf6c42-0c85-418f-af6c-3e4e5b1328f2")
... )
... ).show(truncate=False)
+-----------------------------------------------------------------------------+
|java_method(java.util.UUID, fromString, a5cf6c42-0c85-418f-af6c-3e4e5b1328f2)|
+-----------------------------------------------------------------------------+
|a5cf6c42-0c85-418f-af6c-3e4e5b1328f2 |
+-----------------------------------------------------------------------------+
Example 2: Reflecting a method call with a column name argument
>>> import pyspark.sql.functions as sf
>>> df = spark.createDataFrame([('a5cf6c42-0c85-418f-af6c-3e4e5b1328f2',)], ['a'])
>>> df.select(
... sf.java_method(sf.lit('java.util.UUID'), sf.lit('fromString'), 'a')
... ).show(truncate=False)
+------------------------------------------+
|java_method(java.util.UUID, fromString, a)|
+------------------------------------------+
|a5cf6c42-0c85-418f-af6c-3e4e5b1328f2 |
+------------------------------------------+
"""
return _invoke_function_over_seq_of_columns("java_method", cols)
@_try_remote_functions
def try_reflect(*cols: "ColumnOrName") -> Column:
"""
This is a special version of `reflect` that performs the same operation, but returns a NULL
value instead of raising an error if the invoke method thrown exception.
.. versionadded:: 4.0.0
Parameters
----------
cols : :class:`~pyspark.sql.Column` or column name
the first element should be a Column representing literal string for the class name,
and the second element should be a Column representing literal string for the method name,
and the remaining are input arguments (Columns or column names) to the Java method.
See Also
--------
:meth:`pyspark.sql.functions.reflect`
:meth:`pyspark.sql.functions.java_method`
Examples
--------
Example 1: Reflecting a method call with arguments
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("a5cf6c42-0c85-418f-af6c-3e4e5b1328f2",)], ["a"])
>>> df.select(
... sf.try_reflect(sf.lit("java.util.UUID"), sf.lit("fromString"), "a")
... ).show(truncate=False)
+------------------------------------------+
|try_reflect(java.util.UUID, fromString, a)|
+------------------------------------------+
|a5cf6c42-0c85-418f-af6c-3e4e5b1328f2 |
+------------------------------------------+
Example 2: Exception in the reflection call, resulting in null
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(
... sf.try_reflect(sf.lit("scala.Predef"), sf.lit("require"), sf.lit(False))
... ).show(truncate=False)
+-----------------------------------------+
|try_reflect(scala.Predef, require, false)|
+-----------------------------------------+
|NULL |
+-----------------------------------------+
"""
return _invoke_function_over_seq_of_columns("try_reflect", cols)
@_try_remote_functions
def version() -> Column:
"""
Returns the Spark version. The string contains 2 fields, the first being a release version
and the second being a git revision.
.. versionadded:: 3.5.0
Examples
--------
>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(sf.version()).show(truncate=False) # doctest: +SKIP
+----------------------------------------------+
|version() |
+----------------------------------------------+
|4.0.0 4f8d1f575e99aeef8990c63a9614af0fc5479330|
+----------------------------------------------+
"""
return _invoke_function_over_columns("version")
@_try_remote_functions
def typeof(col: "ColumnOrName") -> Column:
"""
Return DDL-formatted type string for the data type of the input.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(True, 1, 1.0, 'xyz',)], ['a', 'b', 'c', 'd'])
>>> df.select(sf.typeof(df.a), sf.typeof(df.b), sf.typeof('c'), sf.typeof('d')).show()
+---------+---------+---------+---------+
|typeof(a)|typeof(b)|typeof(c)|typeof(d)|
+---------+---------+---------+---------+
| boolean| bigint| double| string|
+---------+---------+---------+---------+
"""
return _invoke_function_over_columns("typeof", col)
@_try_remote_functions
def stack(*cols: "ColumnOrName") -> Column:
"""
Separates `col1`, ..., `colk` into `n` rows. Uses column names col0, col1, etc. by default
unless specified otherwise.
.. versionadded:: 3.5.0
Parameters
----------
cols : :class:`~pyspark.sql.Column` or column name
the first element should be a literal int for the number of rows to be separated,
and the remaining are input elements to be separated.
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1, 2, 3)], ['a', 'b', 'c'])
>>> df.select('*', sf.stack(sf.lit(2), df.a, df.b, 'c')).show()
+---+---+---+----+----+
| a| b| c|col0|col1|
+---+---+---+----+----+
| 1| 2| 3| 1| 2|
| 1| 2| 3| 3|NULL|
+---+---+---+----+----+
>>> df.select('*', sf.stack(sf.lit(2), df.a, df.b, 'c').alias('x', 'y')).show()
+---+---+---+---+----+
| a| b| c| x| y|
+---+---+---+---+----+
| 1| 2| 3| 1| 2|
| 1| 2| 3| 3|NULL|
+---+---+---+---+----+
>>> df.select('*', sf.stack(sf.lit(3), df.a, df.b, 'c')).show()
+---+---+---+----+
| a| b| c|col0|
+---+---+---+----+
| 1| 2| 3| 1|
| 1| 2| 3| 2|
| 1| 2| 3| 3|
+---+---+---+----+
>>> df.select('*', sf.stack(sf.lit(4), df.a, df.b, 'c')).show()
+---+---+---+----+
| a| b| c|col0|
+---+---+---+----+
| 1| 2| 3| 1|
| 1| 2| 3| 2|
| 1| 2| 3| 3|
| 1| 2| 3|NULL|
+---+---+---+----+
"""
return _invoke_function_over_seq_of_columns("stack", cols)
@_try_remote_functions
def bitmap_bit_position(col: "ColumnOrName") -> Column:
"""
Returns the bit position for the given input column.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The input column.
See Also
--------
:meth:`pyspark.sql.functions.bitmap_bucket_number`
:meth:`pyspark.sql.functions.bitmap_construct_agg`
:meth:`pyspark.sql.functions.bitmap_count`
:meth:`pyspark.sql.functions.bitmap_or_agg`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(123,)], ['a'])
>>> df.select('*', sf.bitmap_bit_position('a')).show()
+---+----------------------+
| a|bitmap_bit_position(a)|
+---+----------------------+
|123| 122|
+---+----------------------+
"""
return _invoke_function_over_columns("bitmap_bit_position", col)
@_try_remote_functions
def bitmap_bucket_number(col: "ColumnOrName") -> Column:
"""
Returns the bucket number for the given input column.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The input column.
See Also
--------
:meth:`pyspark.sql.functions.bitmap_bit_position`
:meth:`pyspark.sql.functions.bitmap_construct_agg`
:meth:`pyspark.sql.functions.bitmap_count`
:meth:`pyspark.sql.functions.bitmap_or_agg`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(123,)], ['a'])
>>> df.select('*', sf.bitmap_bucket_number('a')).show()
+---+-----------------------+
| a|bitmap_bucket_number(a)|
+---+-----------------------+
|123| 1|
+---+-----------------------+
"""
return _invoke_function_over_columns("bitmap_bucket_number", col)
@_try_remote_functions
def bitmap_construct_agg(col: "ColumnOrName") -> Column:
"""
Returns a bitmap with the positions of the bits set from all the values from the input column.
The input column will most likely be bitmap_bit_position().
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The input column will most likely be bitmap_bit_position().
See Also
--------
:meth:`pyspark.sql.functions.bitmap_bit_position`
:meth:`pyspark.sql.functions.bitmap_bucket_number`
:meth:`pyspark.sql.functions.bitmap_count`
:meth:`pyspark.sql.functions.bitmap_or_agg`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([(1,),(2,),(3,)], ["a"])
>>> df.select(
... sf.bitmap_construct_agg(sf.bitmap_bit_position('a'))
... ).show()
+--------------------------------------------+
|bitmap_construct_agg(bitmap_bit_position(a))|
+--------------------------------------------+
| [07 00 00 00 00 0...|
+--------------------------------------------+
"""
return _invoke_function_over_columns("bitmap_construct_agg", col)
@_try_remote_functions
def bitmap_count(col: "ColumnOrName") -> Column:
"""
Returns the number of set bits in the input bitmap.
.. versionadded:: 3.5.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The input bitmap.
See Also
--------
:meth:`pyspark.sql.functions.bitmap_bit_position`
:meth:`pyspark.sql.functions.bitmap_bucket_number`
:meth:`pyspark.sql.functions.bitmap_construct_agg`
:meth:`pyspark.sql.functions.bitmap_or_agg`
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("FFFF",)], ["a"])
>>> df.select(sf.bitmap_count(sf.to_binary(df.a, sf.lit("hex")))).show()
+-------------------------------+
|bitmap_count(to_binary(a, hex))|
+-------------------------------+
| 16|
+-------------------------------+
"""
return _invoke_function_over_columns("bitmap_count", col)
@_try_remote_functions
def bitmap_or_agg(col: "ColumnOrName") -> Column:
"""
Returns a bitmap that is the bitwise OR of all of the bitmaps from the input column.
The input column should be bitmaps created from bitmap_construct_agg().
.. versionadded:: 3.5.0
See Also
--------
:meth:`pyspark.sql.functions.bitmap_bit_position`
:meth:`pyspark.sql.functions.bitmap_bucket_number`
:meth:`pyspark.sql.functions.bitmap_construct_agg`
:meth:`pyspark.sql.functions.bitmap_count`
Parameters
----------
col : :class:`~pyspark.sql.Column` or column name
The input column should be bitmaps created from bitmap_construct_agg().
Examples
--------
>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("10",),("20",),("40",)], ["a"])
>>> df.select(sf.bitmap_or_agg(sf.to_binary(df.a, sf.lit("hex")))).show()
+--------------------------------+
|bitmap_or_agg(to_binary(a, hex))|
+--------------------------------+
| [70 00 00 00 00 0...|
+--------------------------------+
"""
return _invoke_function_over_columns("bitmap_or_agg", col)
# ---------------------------- User Defined Function ----------------------------------
@overload
def udf(
f: Callable[..., Any],
returnType: "DataTypeOrString" = StringType(),
*,
useArrow: Optional[bool] = None,
) -> "UserDefinedFunctionLike":
...
@overload
def udf(
f: Optional["DataTypeOrString"] = None,
*,
useArrow: Optional[bool] = None,
) -> Callable[[Callable[..., Any]], "UserDefinedFunctionLike"]:
...
@overload
def udf(
*,
returnType: "DataTypeOrString" = StringType(),
useArrow: Optional[bool] = None,
) -> Callable[[Callable[..., Any]], "UserDefinedFunctionLike"]:
...
@_try_remote_functions
def udf(
f: Optional[Union[Callable[..., Any], "DataTypeOrString"]] = None,
returnType: "DataTypeOrString" = StringType(),
*,
useArrow: Optional[bool] = None,
) -> Union["UserDefinedFunctionLike", Callable[[Callable[..., Any]], "UserDefinedFunctionLike"]]:
"""Creates a user defined function (UDF).
.. versionadded:: 1.3.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. versionchanged:: 4.0.0
Supports keyword-arguments.
Parameters
----------
f : function, optional
python function if used as a standalone function
.. versionchanged:: 4.1.0
Supports vectorized function by specifiying the type hints.
returnType : :class:`pyspark.sql.types.DataType` or str, optional
the return type of the user-defined function. The value can be either a
:class:`pyspark.sql.types.DataType` object or a DDL-formatted type string.
Defaults to :class:`StringType`.
useArrow : bool, optional
whether to use Arrow to optimize the (de)serialization. When it is None, the
Spark config "spark.sql.execution.pythonUDF.arrow.enabled" takes effect.
Examples
--------
>>> from pyspark.sql.types import IntegerType
>>> slen = udf(lambda s: len(s), IntegerType())
>>> @udf
... def to_upper(s):
... if s is not None:
... return s.upper()
...
>>> @udf(returnType=IntegerType())
... def add_one(x):
... if x is not None:
... return x + 1
...
>>> df = spark.createDataFrame([(1, "John Doe", 21)], ("id", "name", "age"))
>>> df.select(slen("name").alias("slen(name)"), to_upper("name"), add_one("age")).show()
+----------+--------------+------------+
|slen(name)|to_upper(name)|add_one(age)|
+----------+--------------+------------+
| 8| JOHN DOE| 22|
+----------+--------------+------------+
UDF can use keyword arguments:
>>> @udf(returnType=IntegerType())
... def calc(a, b):
... return a + 10 * b
...
>>> spark.range(2).select(calc(b=col("id") * 10, a=col("id"))).show()
+-----------------------------+
|calc(b => (id * 10), a => id)|
+-----------------------------+
| 0|
| 101|
+-----------------------------+
Support vectorized function by specifiying the type hints.
To define a vectorized function, the function should meet following requirements:
1, have at least 1 argument. 0-arg is not supported;
2, the type hints should match one of the patterns of pandas UDFs and arrow UDFs;
3, argument `useArrow` should not be explictly set;
If a function doesn't meet the requirements, the function should be treated as a
vanilla python UDF or arrow-optimized python UDF (depending on argument `useArrow`,
configuration `spark.sql.execution.pythonUDF.arrow.enabled`, and dependency installations)
For example, define a 'Series to Series' type pandas UDF.
>>> from pyspark.sql.functions import udf, PandasUDFType
>>> import pandas as pd
>>> @udf(returnType=IntegerType())
... def pd_calc(a: pd.Series, b: pd.Series) -> pd.Series:
... return a + 10 * b
...
>>> pd_calc.evalType == PandasUDFType.SCALAR
True
>>> spark.range(2).select(pd_calc(b=col("id") * 10, a="id")).show()
+--------------------------------+
|pd_calc(b => (id * 10), a => id)|
+--------------------------------+
| 0|
| 101|
+--------------------------------+
For another example, define a 'Array to Array' type arrow UDF.
>>> from pyspark.sql.functions import udf, ArrowUDFType
>>> import pyarrow as pa
>>> @udf(returnType=IntegerType())
... def pa_calc(a: pa.Array, b: pa.Array) -> pa.Array:
... return pa.compute.add(a, pa.compute.multiply(b, 10))
...
>>> pa_calc.evalType == ArrowUDFType.SCALAR
True
>>> spark.range(2).select(pa_calc(b=col("id") * 10, a="id")).show()
+--------------------------------+
|pa_calc(b => (id * 10), a => id)|
+--------------------------------+
| 0|
| 101|
+--------------------------------+
See Also
--------
:meth:`pyspark.sql.functions.pandas_udf`
:meth:`pyspark.sql.functions.arrow_udf`
Notes
-----
The user-defined functions are considered deterministic by default. Due to
optimization, duplicate invocations may be eliminated or the function may even be invoked
more times than it is present in the query. If your function is not deterministic, call
`asNondeterministic` on the user defined function. E.g.:
>>> from pyspark.sql.types import IntegerType
>>> import random
>>> random_udf = udf(lambda: int(random.random() * 100), IntegerType()).asNondeterministic()
The user-defined functions do not support conditional expressions or short circuiting
in boolean expressions and it ends up with being executed all internally. If the functions
can fail on special rows, the workaround is to incorporate the condition into the functions.
The user-defined functions do not take keyword arguments on the calling side.
"""
# The following table shows most of Python data and SQL type conversions in normal UDFs that
# are not yet visible to the user. Some of behaviors are buggy and might be changed in the near
# future. The table might have to be eventually documented externally.
# Please see python/pyspark/sql/tests/udf_type_tests for type tests and golden files
# decorator @udf, @udf(), @udf(dataType())
if f is None or isinstance(f, (str, DataType)):
# If DataType has been passed as a positional argument
# for decorator use it as a returnType
return_type = f or returnType
return functools.partial(
_create_py_udf,
returnType=return_type,
useArrow=useArrow,
)
else:
return _create_py_udf(f=f, returnType=returnType, useArrow=useArrow)
@_try_remote_functions
def udtf(
cls: Optional[Type] = None,
*,
returnType: Optional[Union[StructType, str]] = None,
useArrow: Optional[bool] = None,
) -> Union["UserDefinedTableFunction", Callable[[Type], "UserDefinedTableFunction"]]:
"""Creates a user defined table function (UDTF).
.. versionadded:: 3.5.0
.. versionchanged:: 4.0.0
Supports Python side analysis.
.. versionchanged:: 4.0.0
Supports keyword-arguments.
Parameters
----------
cls : class, optional
the Python user-defined table function handler class.
returnType : :class:`pyspark.sql.types.StructType` or str, optional
the return type of the user-defined table function. The value can be either a
:class:`pyspark.sql.types.StructType` object or a DDL-formatted struct type string.
If None, the handler class must provide `analyze` static method.
useArrow : bool, optional
whether to use Arrow to optimize the (de)serializations. When it's set to None, the
Spark config "spark.sql.execution.pythonUDTF.arrow.enabled" is used.
Examples
--------
Implement the UDTF class and create a UDTF:
>>> class TestUDTF:
... def eval(self, *args: Any):
... yield "hello", "world"
...
>>> from pyspark.sql.functions import udtf
>>> test_udtf = udtf(TestUDTF, returnType="c1: string, c2: string")
>>> test_udtf().show()
+-----+-----+
| c1| c2|
+-----+-----+
|hello|world|
+-----+-----+
UDTF can also be created using the decorator syntax:
>>> @udtf(returnType="c1: int, c2: int")
... class PlusOne:
... def eval(self, x: int):
... yield x, x + 1
...
>>> from pyspark.sql.functions import lit
>>> PlusOne(lit(1)).show()
+---+---+
| c1| c2|
+---+---+
| 1| 2|
+---+---+
UDTF can also have `analyze` static method instead of a static return type:
The `analyze` static method should take arguments:
- The number and order of arguments are the same as the UDTF inputs
- Each argument is a :class:`pyspark.sql.udtf.AnalyzeArgument`, containing:
- dataType: DataType
- value: Any: the calculated value if the argument is foldable; otherwise None
- isTable: bool: True if the argument is a table argument
and return a :class:`pyspark.sql.udtf.AnalyzeResult`, containing.
- schema: StructType
>>> from pyspark.sql.udtf import AnalyzeArgument, AnalyzeResult
>>> # or from pyspark.sql.functions import AnalyzeArgument, AnalyzeResult
>>> @udtf
... class TestUDTFWithAnalyze:
... @staticmethod
... def analyze(a: AnalyzeArgument, b: AnalyzeArgument) -> AnalyzeResult:
... return AnalyzeResult(StructType().add("a", a.dataType).add("b", b.dataType))
...
... def eval(self, a, b):
... yield a, b
...
>>> TestUDTFWithAnalyze(lit(1), lit("x")).show()
+---+---+
| a| b|
+---+---+
| 1| x|
+---+---+
UDTF can use keyword arguments:
>>> @udtf
... class TestUDTFWithKwargs:
... @staticmethod
... def analyze(
... a: AnalyzeArgument, b: AnalyzeArgument, **kwargs: AnalyzeArgument
... ) -> AnalyzeResult:
... return AnalyzeResult(
... StructType().add("a", a.dataType)
... .add("b", b.dataType)
... .add("x", kwargs["x"].dataType)
... )
...
... def eval(self, a, b, **kwargs):
... yield a, b, kwargs["x"]
...
>>> TestUDTFWithKwargs(lit(1), x=lit("x"), b=lit("b")).show()
+---+---+---+
| a| b| x|
+---+---+---+
| 1| b| x|
+---+---+---+
>>> _ = spark.udtf.register("test_udtf", TestUDTFWithKwargs)
>>> spark.sql("SELECT * FROM test_udtf(1, x => 'x', b => 'b')").show()
+---+---+---+
| a| b| x|
+---+---+---+
| 1| b| x|
+---+---+---+
Arrow optimization can be explicitly enabled when creating UDTFs:
>>> @udtf(returnType="c1: int, c2: int", useArrow=True)
... class ArrowPlusOne:
... def eval(self, x: int):
... yield x, x + 1
...
>>> ArrowPlusOne(lit(1)).show()
+---+---+
| c1| c2|
+---+---+
| 1| 2|
+---+---+
Notes
-----
User-defined table functions (UDTFs) are considered non-deterministic by default.
Use `asDeterministic()` to mark a function as deterministic. E.g.:
>>> class PlusOne:
... def eval(self, a: int):
... yield a + 1,
>>> plus_one = udtf(PlusOne, returnType="r: int").asDeterministic()
Use "yield" to produce one row for the UDTF result relation as many times
as needed. In the context of a lateral join, each such result row will be
associated with the most recent input row consumed from the "eval" method.
User-defined table functions are considered opaque to the optimizer by default.
As a result, operations like filters from WHERE clauses or limits from
LIMIT/OFFSET clauses that appear after the UDTF call will execute on the
UDTF's result relation. By the same token, any relations forwarded as input
to UDTFs will plan as full table scans in the absence of any explicit such
filtering or other logic explicitly written in a table subquery surrounding the
provided input relation.
User-defined table functions do not accept keyword arguments on the calling side.
"""
if cls is None:
return functools.partial(_create_py_udtf, returnType=returnType, useArrow=useArrow)
else:
return _create_py_udtf(cls=cls, returnType=returnType, useArrow=useArrow)
@_try_remote_functions
def arrow_udtf(
cls: Optional[Type] = None,
*,
returnType: Optional[Union[StructType, str]] = None,
) -> Union["UserDefinedTableFunction", Callable[[Type], "UserDefinedTableFunction"]]:
"""Creates a PyArrow-native user defined table function (UDTF).
This function provides a PyArrow-native interface for UDTFs, where the eval method
receives PyArrow RecordBatches or Arrays and returns an Iterator of PyArrow Tables
or RecordBatches.
This enables true vectorized computation without row-by-row processing overhead.
.. versionadded:: 4.1.0
Parameters
----------
cls : class, optional
the Python user-defined table function handler class.
returnType : :class:`pyspark.sql.types.StructType` or str, optional
the return type of the user-defined table function. The value can be either a
:class:`pyspark.sql.types.StructType` object or a DDL-formatted struct type string.
Examples
--------
UDTF with PyArrow RecordBatch input:
>>> import pyarrow as pa
>>> from pyspark.sql.functions import arrow_udtf
>>> @arrow_udtf(returnType="x int, y int")
... class MyUDTF:
... def eval(self, batch: pa.RecordBatch):
... # Process the entire batch vectorized
... x_array = batch.column('x')
... y_array = batch.column('y')
... result_table = pa.table({
... 'x': x_array,
... 'y': y_array
... })
... yield result_table
...
>>> df = spark.range(10).selectExpr("id as x", "id as y")
>>> MyUDTF(df.asTable()).show() # doctest: +SKIP
UDTF with PyArrow Array inputs:
>>> @arrow_udtf(returnType="x int, y int")
... class MyUDTF2:
... def eval(self, x: pa.Array, y: pa.Array):
... # Process arrays vectorized
... result_table = pa.table({
... 'x': x,
... 'y': y
... })
... yield result_table
...
>>> MyUDTF2(lit(1), lit(2)).show() # doctest: +SKIP
Notes
-----
- The eval method must accept PyArrow RecordBatches or Arrays as input
- The eval method must yield PyArrow Tables or RecordBatches as output
"""
if cls is None:
return functools.partial(_create_pyarrow_udtf, returnType=returnType)
else:
return _create_pyarrow_udtf(cls=cls, returnType=returnType)
def _test() -> None:
import doctest
from pyspark.sql import SparkSession
import pyspark.sql.functions.builtin
globs = pyspark.sql.functions.builtin.__dict__.copy()
spark = (
SparkSession.builder.master("local[4]").appName("sql.functions.builtin tests").getOrCreate()
)
sc = spark.sparkContext
globs["sc"] = sc
globs["spark"] = spark
(failure_count, test_count) = doctest.testmod(
pyspark.sql.functions.builtin,
globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()